Snowflake connector¶

In [1]:
import pandas as pd
import os
import snowflake.connector
import seaborn as sns
import matplotlib.pyplot as plt 
import numpy as np
import plotly 
import scipy.stats as stats
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.neighbors import NearestNeighbors
In [2]:
#Enter the password you use to log into snowflake
import getpass 
pwd = getpass.getpass("enter password")
enter password········
In [3]:
#Enter the username of your snowflake account and for account use the url data before .snowflakecomputing.com
#warehouse name can be found by clicking on warehouse on the right hand side of the run button on snowflake
#schema should be public but you can also check for it next to the warehouse button

import snowflake.connector 
conn = snowflake.connector.connect(user='AL4129',
                                   password=str(pwd),
                                   account='szfezvd-columbia',
                                   warehouse='WAREHOUSE',
                                   schema='PUBLIC')
In [4]:
#create a query to pull data from snowflake into your dataset 
#You can use any query you'd typically use to isolate the data you need

q1 = """
WITH BASE AS -- BASE 
(
SELECT *
FROM (
SELECT TI.CM_TRACK, TI.TRACK_NAME, TI.CM_ALBUM,
       TI.ALBUM_NAME, TI.RELEASE_DATE, AI.ARTIST_NAME,
       AI.ARTIST_COUNTRY_CODE2, AI.COUNTRY_NAME, AI.GENRE_ID,
       AI.ARTIST_GENRE, 
       ROW_NUMBER() OVER(PARTITION BY TI.CM_TRACK ORDER BY TI.CM_TRACK DESC) DUPE 
FROM "CHARTMETRIC"."SHARES"."CAPSTONE_TRACK_INFO" TI
LEFT JOIN "CHARTMETRIC"."SHARES"."CAPSTONE_TRACK_ARTIST_INFO" AI 
ON TI.CM_TRACK = AI.CM_TRACK
LEFT JOIN "CHARTMETRIC"."SHARES"."CAPSTONE_TRACK_STATS" TS
ON TI.CM_TRACK = TS.CM_TRACK
WHERE TI.CM_TRACK NOT IN 
   (SELECT DISTINCT CM_TRACK
    FROM (
          SELECT CM_TRACK, 
          COALESCE (SHAZAM_COUNT, 0) AS SHAZAM_COUNT,
          COALESCE (TIKTOK_POSTS, 0) AS TIKTOK_POSTS 
          FROM "CHARTMETRIC"."SHARES"."CAPSTONE_TRACK_STATS")
     WHERE SHAZAM_COUNT >= 10000
   )
AND TI.CM_TRACK NOT IN 
   (SELECT DISTINCT CM_TRACK
    FROM (
          SELECT CM_TRACK, 
          COALESCE (SHAZAM_COUNT, 0) AS SHAZAM_COUNT,
          COALESCE (TIKTOK_POSTS, 0) AS TIKTOK_POSTS 
          FROM "CHARTMETRIC"."SHARES"."CAPSTONE_TRACK_STATS")
     WHERE TIKTOK_POSTS >= 10000
   )
)
WHERE DUPE = 1
),

T0 AS (
SELECT CM_TRACK,ARTIST_GENRE
FROM (
      SELECT CM_TRACK, ARTIST_GENRE, ROW_NUMBER() OVER (PARTITION BY CM_TRACK ORDER BY ARTIST_GENRE) AS rnk
      FROM "CHARTMETRIC"."SHARES"."CAPSTONE_TRACK_ARTIST_INFO") AS t
      WHERE rnk = 1),


T AS (
SELECT c.CM_TRACK, YOUTUBE_VIEWS, YOUTUBE_LIKES, SPOTIFY_PLAYS, 
        SPOTIFY_POPULARITY, TIKTOK_POSTS, AIRPLAY_STREAMS, T0.ARTIST_GENRE, 
        CREATED_AT, EXTRACT(MONTH FROM CREATED_AT) AS Month, EXTRACT(YEAR FROM CREATED_AT) AS Year
FROM "CHARTMETRIC"."SHARES"."CAPSTONE_TRACK_STATS" AS c LEFT JOIN T0
USING (CM_TRACK)),

T1 AS (
SELECT CM_TRACK, CREATED_AT, Month,
        CASE WHEN Month IN (1,2,3) THEN 'Q1'
        WHEN Month IN (4,5,6) THEN 'Q2'
        WHEN Month IN (7,8,9) THEN 'Q3'
        ELSE 'Q4' END AS Quarters
FROM T
WHERE Year = 2021),

T2 AS (
SELECT CM_TRACK, MAX(CREATED_AT) AS Last_day, Quarters
FROM T1
GROUP BY CM_TRACK, Quarters
ORDER BY CM_TRACK, QUarters),

T3 AS (
SELECT T.CM_TRACK, T2.Quarters, T2.Last_day, 
        YOUTUBE_VIEWS, LAG(YOUTUBE_VIEWS, 1) OVER(PARTITION BY T.CM_TRACK ORDER BY Quarters) AS Last_YOUTUBE_VIEWS,
        YOUTUBE_LIKES, LAG(YOUTUBE_LIKES, 1) OVER(PARTITION BY T.CM_TRACK ORDER BY Quarters) AS Last_YOUTUBE_LIKES,
        SPOTIFY_PLAYS, LAG(SPOTIFY_PLAYS, 1) OVER(PARTITION BY T.CM_TRACK ORDER BY Quarters) AS Last_SPOTIFY_PLAYS,
        SPOTIFY_POPULARITY, LAG(SPOTIFY_POPULARITY, 1) OVER(PARTITION BY T.CM_TRACK ORDER BY Quarters) AS Last_SPOTIFY_POPULARITY,
        TIKTOK_POSTS, LAG(TIKTOK_POSTS, 1) OVER(PARTITION BY T.CM_TRACK ORDER BY Quarters) AS Last_TIKTOK_POSTS,
        AIRPLAY_STREAMS, LAG(AIRPLAY_STREAMS, 1) OVER(PARTITION BY T.CM_TRACK ORDER BY Quarters) AS Last_AIRPLAY_STREAMS,
        ARTIST_GENRE
FROM T JOIN T2 
ON T.CM_TRACK = T2.CM_TRACK AND T.CREATED_AT = T2.Last_day
ORDER BY T.CM_TRACK, T2.Quarters),

Q1_growth AS (
SELECT T3.CM_TRACK, IFNULL(T3.YOUTUBE_VIEWS - c.MIN_YT_L,0) AS Q1_YOUTUBE_VIEWS_GROWTH, 
        IFNULL(T3.YOUTUBE_LIKES - c.MIN_YT_L,0) AS Q1_YOUTUBE_LIKES_GROWTH,
        IFNULL(T3.SPOTIFY_PLAYS - c.MIN_SPOT_P,0) AS Q1_SPOTIFY_PLAYS_GROWTH,
        IFNULL(T3.SPOTIFY_POPULARITY - c.MIN_SPOT_PP,0) AS Q1_SPOTIFY_POPULARITY_GROWTH,
        IFNULL(T3.TIKTOK_POSTS - c.MIN_TT,0) AS Q1_TIKTOK_POSTS_GROWTH,
        IFNULL(T3.AIRPLAY_STREAMS - c.MIN_AP,0) AS Q1_AIRPLAY_STREAMS_GROWTH, ARTIST_GENRE
FROM T3 LEFT JOIN (SELECT CM_TRACK, MIN(YOUTUBE_VIEWS) AS MIN_YT_V, 
                   MIN(YOUTUBE_LIKES) AS MIN_YT_L, 
                   MIN(SPOTIFY_PLAYS) AS MIN_SPOT_P, 
                   MIN(SPOTIFY_POPULARITY) AS MIN_SPOT_PP, 
                   MIN(TIKTOK_POSTS) AS MIN_TT, 
                   MIN(AIRPLAY_STREAMS) AS MIN_AP 
                   FROM (
                         SELECT CM_TRACK, YOUTUBE_VIEWS, YOUTUBE_LIKES,
                         SPOTIFY_PLAYS, SPOTIFY_POPULARITY, TIKTOK_POSTS, 
                         AIRPLAY_STREAMS, CREATED_AT, Month, 
                         CASE WHEN Month IN (1,2,3) THEN 'Q1'
                         WHEN Month IN (4,5,6) THEN 'Q2'
                         WHEN Month IN (7,8,9) THEN 'Q3'
                         ELSE 'Q4' END AS Quarters
                         FROM (
                               SELECT CM_TRACK, YOUTUBE_VIEWS, YOUTUBE_LIKES,
                                      SPOTIFY_PLAYS, SPOTIFY_POPULARITY, TIKTOK_POSTS, 
                                      AIRPLAY_STREAMS, CREATED_AT, 
                                      EXTRACT(MONTH FROM CREATED_AT) AS Month, EXTRACT(YEAR FROM CREATED_AT) AS Year
                               FROM "CHARTMETRIC"."SHARES"."CAPSTONE_TRACK_STATS"
                               WHERE CREATED_AT BETWEEN '2021-01-01' AND '2021-12-31'
                               )
                        )
                  WHERE Quarters = 'Q1'
                  GROUP BY CM_TRACK
                  ) AS c
USING (CM_TRACK)
WHERE Quarters = 'Q1'),

Q2_Growth AS(
SELECT T3.CM_TRACK, IFNULL(T3.YOUTUBE_VIEWS - c.YOUTUBE_VIEWS,0) AS Q2_YOUTUBE_VIEWS_GROWTH, 
        IFNULL(T3.YOUTUBE_LIKES - c.YOUTUBE_LIKES,0) AS Q2_YOUTUBE_LIKES_GROWTH,
        IFNULL(T3.SPOTIFY_PLAYS - c.SPOTIFY_PLAYS,0) AS Q2_SPOTIFY_PLAYS_GROWTH,
        IFNULL(T3.SPOTIFY_POPULARITY - c.SPOTIFY_POPULARITY,0) AS Q2_SPOTIFY_POPULARITY_GROWTH,
        IFNULL(T3.TIKTOK_POSTS - c.TIKTOK_POSTS,0) AS Q2_TIKTOK_POSTS_GROWTH,
        IFNULL(T3.AIRPLAY_STREAMS - c.AIRPLAY_STREAMS,0) AS Q2_AIRPLAY_STREAMS_GROWTH, ARTIST_GENRE
FROM T3 LEFT JOIN (SELECT CM_TRACK, YOUTUBE_VIEWS, YOUTUBE_LIKES, SPOTIFY_PLAYS, SPOTIFY_POPULARITY, TIKTOK_POSTS, AIRPLAY_STREAMS 
                   FROM T3
                   WHERE Quarters = 'Q1') AS c
USING (CM_TRACK)
WHERE Quarters = 'Q2'),

Q3_Growth AS (
SELECT T3.CM_TRACK, IFNULL(T3.YOUTUBE_VIEWS - c.YOUTUBE_VIEWS,0) AS Q3_YOUTUBE_VIEWS_GROWTH, 
        IFNULL(T3.YOUTUBE_LIKES - c.YOUTUBE_LIKES,0) AS Q3_YOUTUBE_LIKES_GROWTH,
        IFNULL(T3.SPOTIFY_PLAYS - c.SPOTIFY_PLAYS,0) AS Q3_SPOTIFY_PLAYS_GROWTH,
        IFNULL(T3.SPOTIFY_POPULARITY - c.SPOTIFY_POPULARITY,0) AS Q3_SPOTIFY_POPULARITY_GROWTH,
        IFNULL(T3.TIKTOK_POSTS - c.TIKTOK_POSTS,0) AS Q3_TIKTOK_POSTS_GROWTH,
        IFNULL(T3.AIRPLAY_STREAMS - c.AIRPLAY_STREAMS,0) AS Q3_AIRPLAY_STREAMS_GROWTH, ARTIST_GENRE
FROM T3 LEFT JOIN (SELECT CM_TRACK, YOUTUBE_VIEWS, YOUTUBE_LIKES, SPOTIFY_PLAYS, SPOTIFY_POPULARITY, TIKTOK_POSTS, AIRPLAY_STREAMS 
                   FROM T3
                   WHERE Quarters = 'Q2') AS c
USING (CM_TRACK)
WHERE Quarters = 'Q3'),

Q4_Growth AS (
SELECT T3.CM_TRACK, IFNULL(T3.YOUTUBE_VIEWS - c.YOUTUBE_VIEWS,0) AS Q4_YOUTUBE_VIEWS_GROWTH, 
        IFNULL(T3.YOUTUBE_LIKES - c.YOUTUBE_LIKES,0) AS Q4_YOUTUBE_LIKES_GROWTH,
        IFNULL(T3.SPOTIFY_PLAYS - c.SPOTIFY_PLAYS,0) AS Q4_SPOTIFY_PLAYS_GROWTH,
        IFNULL(T3.SPOTIFY_POPULARITY - c.SPOTIFY_POPULARITY,0) AS Q4_SPOTIFY_POPULARITY_GROWTH,
        IFNULL(T3.TIKTOK_POSTS - c.TIKTOK_POSTS,0) AS Q4_TIKTOK_POSTS_GROWTH,
        IFNULL(T3.AIRPLAY_STREAMS - c.AIRPLAY_STREAMS,0) AS Q4_AIRPLAY_STREAMS_GROWTH, ARTIST_GENRE
FROM T3 LEFT JOIN (SELECT CM_TRACK, YOUTUBE_VIEWS, YOUTUBE_LIKES, SPOTIFY_PLAYS, SPOTIFY_POPULARITY, TIKTOK_POSTS, AIRPLAY_STREAMS 
                   FROM T3
                   WHERE Quarters = 'Q3') AS c
USING (CM_TRACK)
WHERE Quarters = 'Q4'),


QUARTERS_FINAL AS (
SELECT Q4_Growth.CM_TRACK, Q4_Growth.ARTIST_GENRE, 
        IFNULL(Q1_YOUTUBE_VIEWS_GROWTH, 0) AS Q1_YOUTUBE_VIEWS_GROWTH, IFNULL(Q1_YOUTUBE_LIKES_GROWTH, 0) AS Q1_YOUTUBE_LIKES_GROWTH, 
        IFNULL(Q1_SPOTIFY_PLAYS_GROWTH, 0) AS Q1_SPOTIFY_PLAYS_GROWTH, IFNULL(Q1_SPOTIFY_POPULARITY_GROWTH, 0) AS Q1_SPOTIFY_POPULARITY_GROWTH, 
        IFNULL(Q1_TIKTOK_POSTS_GROWTH, 0) AS Q1_TIKTOK_POSTS_GROWTH, IFNULL(Q1_AIRPLAY_STREAMS_GROWTH, 0) AS Q1_AIRPLAY_STREAMS_GROWTH,
        IFNULL(Q2_YOUTUBE_VIEWS_GROWTH, 0) AS Q2_YOUTUBE_VIEWS_GROWTH, IFNULL(Q2_YOUTUBE_LIKES_GROWTH, 0) AS Q2_YOUTUBE_LIKES_GROWTH, 
        IFNULL(Q2_SPOTIFY_PLAYS_GROWTH, 0) AS Q2_SPOTIFY_PLAYS_GROWTH, IFNULL(Q2_SPOTIFY_POPULARITY_GROWTH, 0) AS Q2_SPOTIFY_POPULARITY_GROWTH, 
        IFNULL(Q2_TIKTOK_POSTS_GROWTH, 0) AS Q2_TIKTOK_POSTS_GROWTH, IFNULL(Q2_AIRPLAY_STREAMS_GROWTH, 0) AS Q2_AIRPLAY_STREAMS_GROWTH,
        IFNULL(Q3_YOUTUBE_VIEWS_GROWTH, 0) AS Q3_YOUTUBE_VIEWS_GROWTH, IFNULL(Q3_YOUTUBE_LIKES_GROWTH, 0) AS Q3_YOUTUBE_LIKES_GROWTH, 
        IFNULL(Q3_SPOTIFY_PLAYS_GROWTH, 0) AS Q3_SPOTIFY_PLAYS_GROWTH, IFNULL(Q3_SPOTIFY_POPULARITY_GROWTH, 0) AS Q3_SPOTIFY_POPULARITY_GROWTH, 
        IFNULL(Q3_TIKTOK_POSTS_GROWTH, 0) AS Q3_TIKTOK_POSTS_GROWTH, IFNULL(Q3_AIRPLAY_STREAMS_GROWTH, 0) AS Q3_AIRPLAY_STREAMS_GROWTH,
        IFNULL(Q4_YOUTUBE_VIEWS_GROWTH, 0) AS Q4_YOUTUBE_VIEWS_GROWTH, IFNULL(Q4_YOUTUBE_LIKES_GROWTH, 0) AS Q4_YOUTUBE_LIKES_GROWTH, 
        IFNULL(Q4_SPOTIFY_PLAYS_GROWTH, 0) AS Q4_SPOTIFY_PLAYS_GROWTH, IFNULL(Q4_SPOTIFY_POPULARITY_GROWTH, 0) AS Q4_SPOTIFY_POPULARITY_GROWTH, 
        IFNULL(Q4_TIKTOK_POSTS_GROWTH, 0) AS Q4_TIKTOK_POSTS_GROWTH, IFNULL(Q4_AIRPLAY_STREAMS_GROWTH, 0) AS Q4_AIRPLAY_STREAMS_GROWTH
FROM Q1_Growth FULL JOIN Q2_Growth
ON Q1_Growth.CM_TRACK = Q2_Growth.CM_TRACK AND Q1_Growth.ARTIST_GENRE = Q2_Growth.ARTIST_GENRE
FULL JOIN Q3_Growth
ON Q2_Growth.CM_TRACK = Q3_Growth.CM_TRACK AND Q2_Growth.ARTIST_GENRE = Q3_Growth.ARTIST_GENRE
FULL JOIN Q4_Growth 
ON Q3_Growth.CM_TRACK = Q4_Growth.CM_TRACK AND Q3_Growth.ARTIST_GENRE = Q4_Growth.ARTIST_GENRE
ORDER BY CM_TRACK, ARTIST_GENRE), 


-- Date table: Find the last date of each month for each CM_Track
Date AS(SELECT CM_TRACK,MONTH(Created_at) as Month,MAX(Created_at) as last_day
                   FROM "CHARTMETRIC"."SHARES"."CAPSTONE_TRACK_STATS"
                   WHERE YEAR(Created_at) ='2021'
                   GROUP BY Month,CM_TRACK
                   ORDER BY 1,2),
-- MonthlyCTE: Join the platforms performance to date data
     MonthlyCTE AS(SELECT a.CM_TRACK,a.Month,a.last_day,b.YOUTUBE_VIEWS,b.YOUTUBE_LIKES,b.SPOTIFY_PLAYS,b.SPOTIFY_POPULARITY,
                          b.TIKTOK_POSTS,b.AIRPLAY_STREAMS
                   FROM Date a
                   LEFT JOIN "CHARTMETRIC"."SHARES"."CAPSTONE_TRACK_STATS" b
                   ON a.CM_TRACK = b.CM_TRACK
                   AND a.last_day = b.created_at
                   WHERE YEAR(Created_at) ='2021'),
-- GenrePick up the representive genre of each track based on artist genre rank
         Genre  AS(SELECT CM_TRACK,artist_genre as genre
                   FROM (SELECT c.CM_TRACK,c.artist_genre,
                                row_number()OVER(PARTITION BY CM_TRACK ORDER BY artist_genre) as rnk
                        FROM "CHARTMETRIC"."SHARES"."CAPSTONE_TRACK_ARTIST_INFO" c) tmp
                   WHERE rnk=1),
-- Genre_MonthlyCTE:Join the representive genre to monthly platforms performance
Genre_MonthlyCTE AS (SELECT g.genre,d.*
                    FROM MonthlyCTE d
                    LEFT JOIN Genre g
                    USING(CM_TRACK)),
--  Pre_MonthlyCTE: Query last monthly data and join them to each month (EG: May/June,June/July...)
 Pre_MonthlyCTE AS(SELECT CM_TRACK,genre,Month,last_day,YOUTUBE_VIEWS,YOUTUBE_LIKES,SPOTIFY_PLAYS,SPOTIFY_POPULARITY,
                          TIKTOK_POSTS,AIRPLAY_STREAMS,
                          LAG(YOUTUBE_VIEWS,1) OVER(PARTITION BY CM_TRACK ORDER BY Month) as prev_month_YTViews,
                          LAG(YOUTUBE_LIKES,1) OVER(PARTITION BY CM_TRACK ORDER BY Month) as prev_month_YTLikes,
                          LAG(SPOTIFY_PLAYS,1) OVER(PARTITION BY CM_TRACK ORDER BY Month) as prev_month_SpotifyPlays,
                          LAG(SPOTIFY_POPULARITY,1) OVER(PARTITION BY CM_TRACK ORDER BY Month) as prev_month_SpotifyPop,
                          LAG(TIKTOK_POSTS,1) OVER(PARTITION BY CM_TRACK ORDER BY Month) as prev_month_TIKTOKPOSTS,
                          LAG(AIRPLAY_STREAMS,1) OVER(PARTITION BY CM_TRACK ORDER BY Month) as prev_month_AIRPLAY
                   FROM Genre_MonthlyCTE),                   
-- Growth Rate Table includes perfomance of platforms and the every montly growth                                           
Growth_Rate AS(SELECT CM_TRACK,genre,month,last_day,YOUTUBE_VIEWS,prev_month_YTViews,
                      YOUTUBE_VIEWS-prev_month_YTViews as YTViews_Growth,
                      YOUTUBE_LIKES,prev_month_YTLikes,
                      YOUTUBE_LIKES-prev_month_YTLikes as YTLikes_Growth,
                      SPOTIFY_PLAYS,prev_month_SpotifyPlays,
                      SPOTIFY_PLAYS-prev_month_SpotifyPlays as SpotifyPlays_Growth,
                      SPOTIFY_POPULARITY,prev_month_SpotifyPop,
                      SPOTIFY_POPULARITY-prev_month_SpotifyPop as SpotifyPop_Growth,
                      TIKTOK_POSTS,prev_month_TIKTOKPOSTS,
                      TIKTOK_POSTS-prev_month_TIKTOKPOSTS as TIKTOK_Growth,
                      AIRPLAY_STREAMS,prev_month_AIRPLAY,
                      AIRPLAY_STREAMS-prev_month_AIRPLAY as AIRPLAY_Growth
                FROM Pre_MonthlyCTE),
     YTViews_Growth AS(SELECT CM_TRACK,genre,month,YTViews_Growth
                      FROM Growth_Rate),
                      
MOM_FINAL AS (
SELECT CM_Track,genre,
       MAX(CASE WHEN month='1' THEN YTViews_Growth ELSE 0 END) AS "M1_YOUTUBE_VIEWS_GROWTH",
       MAX(CASE WHEN month='2' THEN YTViews_Growth ELSE 0 END) AS "M2_YOUTUBE_VIEWS_GROWTH",
       MAX(CASE WHEN month='3' THEN YTViews_Growth ELSE 0 END) AS "M3_YOUTUBE_VIEWS_GROWTH",
       MAX(CASE WHEN month='4' THEN YTViews_Growth ELSE 0 END) AS "M4_YOUTUBE_VIEWS_GROWTH",
       MAX(CASE WHEN month='5' THEN YTViews_Growth ELSE 0 END) AS "M5_YOUTUBE_VIEWS_GROWTH",
       MAX(CASE WHEN month='6' THEN YTViews_Growth ELSE 0 END) AS "M6_YOUTUBE_VIEWS_GROWTH",
       MAX(CASE WHEN month='7' THEN YTViews_Growth ELSE 0 END) AS "M7_YOUTUBE_VIEWS_GROWTH",
       MAX(CASE WHEN month='8' THEN YTViews_Growth ELSE 0 END) AS "M8_YOUTUBE_VIEWS_GROWTH",
       MAX(CASE WHEN month='9' THEN YTViews_Growth ELSE 0 END) AS "M9_YOUTUBE_VIEWS_GROWTH",
       MAX(CASE WHEN month='10' THEN YTViews_Growth ELSE 0 END) AS "M10_YOUTUBE_VIEWS_GROWTH",
       MAX(CASE WHEN month='11' THEN YTViews_Growth ELSE 0 END) AS "M11_YOUTUBE_VIEWS_GROWTH",
       MAX(CASE WHEN month='12' THEN YTViews_Growth ELSE 0 END) AS "M12_YOUTUBE_VIEWS_GROWTH",      
       MAX(CASE WHEN month='1' THEN YTLikes_Growth ELSE 0 END) AS "M1_YOUTUBE_LIKES_GROWTH",
       MAX(CASE WHEN month='2' THEN YTLikes_Growth ELSE 0 END) AS "M2_YOUTUBE_LIKES_GROWTH",
       MAX(CASE WHEN month='3' THEN YTLikes_Growth ELSE 0 END) AS "M3_YOUTUBE_LIKES_GROWTH",
       MAX(CASE WHEN month='4' THEN YTLikes_Growth ELSE 0 END) AS "M4_YOUTUBE_LIKES_GROWTH",
       MAX(CASE WHEN month='5' THEN YTLikes_Growth ELSE 0 END) AS "M5_YOUTUBE_LIKES_GROWTH",
       MAX(CASE WHEN month='6' THEN YTLikes_Growth ELSE 0 END) AS "M6_YOUTUBE_LIKES_GROWTH",
       MAX(CASE WHEN month='7' THEN YTLikes_Growth ELSE 0 END) AS "M7_YOUTUBE_LIKES_GROWTH",
       MAX(CASE WHEN month='8' THEN YTLikes_Growth ELSE 0 END) AS "M8_YOUTUBE_LIKES_GROWTH",
       MAX(CASE WHEN month='9' THEN YTLikes_Growth ELSE 0 END) AS "M9_YOUTUBE_LIKES_GROWTH",
       MAX(CASE WHEN month='10' THEN YTLikes_Growth ELSE 0 END) AS "M10_YOUTUBE_LIKES_GROWTH",
       MAX(CASE WHEN month='11' THEN YTLikes_Growth ELSE 0 END) AS "M11_YOUTUBE_LIKES_GROWTH",
       MAX(CASE WHEN month='12' THEN YTLikes_Growth ELSE 0 END) AS "M12_YOUTUBE_LIKES_GROWTH", 
       MAX(CASE WHEN month='1' THEN SpotifyPlays_Growth ELSE 0 END) AS "M1_SPOTIFY_PLAYS_GROWTH",
       MAX(CASE WHEN month='2' THEN SpotifyPlays_Growth ELSE 0 END) AS "M2_SPOTIFY_PLAYS_GROWTH",
       MAX(CASE WHEN month='3' THEN SpotifyPlays_Growth ELSE 0 END) AS "M3_SPOTIFY_PLAYS_GROWTH",
       MAX(CASE WHEN month='4' THEN SpotifyPlays_Growth ELSE 0 END) AS "M4_SPOTIFY_PLAYS_GROWTH",
       MAX(CASE WHEN month='5' THEN SpotifyPlays_Growth ELSE 0 END) AS "M5_SPOTIFY_PLAYS_GROWTH",
       MAX(CASE WHEN month='6' THEN SpotifyPlays_Growth ELSE 0 END) AS "M6_SPOTIFY_PLAYS_GROWTH",
       MAX(CASE WHEN month='7' THEN SpotifyPlays_Growth ELSE 0 END) AS "M7_SPOTIFY_PLAYS_GROWTH",
       MAX(CASE WHEN month='8' THEN SpotifyPlays_Growth ELSE 0 END) AS "M8_SPOTIFY_PLAYS_GROWTH",
       MAX(CASE WHEN month='9' THEN SpotifyPlays_Growth ELSE 0 END) AS "M9_SPOTIFY_PLAYS_GROWTH",
       MAX(CASE WHEN month='10' THEN SpotifyPlays_Growth ELSE 0 END) AS "M10_SPOTIFY_PLAYS_GROWTH",
       MAX(CASE WHEN month='11' THEN SpotifyPlays_Growth ELSE 0 END) AS "M11_SPOTIFY_PLAYS_GROWTH",
       MAX(CASE WHEN month='12' THEN SpotifyPlays_Growth ELSE 0 END) AS "M12_SPOTIFY_PLAYS_GROWTH",    
       MAX(CASE WHEN month='1' THEN SpotifyPop_Growth ELSE 0 END) AS "M1_SPOTIFY_POPULARITY_GROWTH",
       MAX(CASE WHEN month='2' THEN SpotifyPop_Growth ELSE 0 END) AS "M2_SPOTIFY_POPULARITY_GROWTH",
       MAX(CASE WHEN month='3' THEN SpotifyPop_Growth ELSE 0 END) AS "M3_SPOTIFY_POPULARITY_GROWTH",
       MAX(CASE WHEN month='4' THEN SpotifyPop_Growth ELSE 0 END) AS "M4_SPOTIFY_POPULARITY_GROWTH",
       MAX(CASE WHEN month='5' THEN SpotifyPop_Growth ELSE 0 END) AS "M5_SPOTIFY_POPULARITY_GROWTH",
       MAX(CASE WHEN month='6' THEN SpotifyPop_Growth ELSE 0 END) AS "M6_SPOTIFY_POPULARITY_GROWTH",
       MAX(CASE WHEN month='7' THEN SpotifyPop_Growth ELSE 0 END) AS "M7_SPOTIFY_POPULARITY_GROWTH",
       MAX(CASE WHEN month='8' THEN SpotifyPop_Growth ELSE 0 END) AS "M8_SPOTIFY_POPULARITY_GROWTH",
       MAX(CASE WHEN month='9' THEN SpotifyPop_Growth ELSE 0 END) AS "M9_SPOTIFY_POPULARITY_GROWTH",
       MAX(CASE WHEN month='10' THEN SpotifyPop_Growth ELSE 0 END) AS "M10_SPOTIFY_POPULARITY_GROWTH",
       MAX(CASE WHEN month='11' THEN SpotifyPop_Growth ELSE 0 END) AS "M11_SPOTIFY_POPULARITY_GROWTH",
       MAX(CASE WHEN month='12' THEN SpotifyPop_Growth ELSE 0 END) AS "M12_SPOTIFY_POPULARITY_GROWTH",
       MAX(CASE WHEN month='1' THEN TIKTOK_Growth ELSE 0 END) AS "M1_TIKTOK_POSTS_GROWTH",
       MAX(CASE WHEN month='2' THEN TIKTOK_Growth ELSE 0 END) AS "M2_TIKTOK_POSTS_GROWTH",
       MAX(CASE WHEN month='3' THEN TIKTOK_Growth ELSE 0 END) AS "M3_TIKTOK_POSTS_GROWTH",
       MAX(CASE WHEN month='4' THEN TIKTOK_Growth ELSE 0 END) AS "M4_TIKTOK_POSTS_GROWTH",
       MAX(CASE WHEN month='5' THEN TIKTOK_Growth ELSE 0 END) AS "M5_TIKTOK_POSTS_GROWTH",
       MAX(CASE WHEN month='6' THEN TIKTOK_Growth ELSE 0 END) AS "M6_TIKTOK_POSTS_GROWTH",
       MAX(CASE WHEN month='7' THEN TIKTOK_Growth ELSE 0 END) AS "M7_TIKTOK_POSTS_GROWTH",
       MAX(CASE WHEN month='8' THEN TIKTOK_Growth ELSE 0 END) AS "M8_TIKTOK_POSTS_GROWTH",
       MAX(CASE WHEN month='9' THEN TIKTOK_Growth ELSE 0 END) AS "M9_TIKTOK_POSTS_GROWTH",
       MAX(CASE WHEN month='10' THEN TIKTOK_Growth ELSE 0 END) AS "M10_TIKTOK_POSTS_GROWTH",
       MAX(CASE WHEN month='11' THEN TIKTOK_Growth ELSE 0 END) AS "M11_TIKTOK_POSTS_GROWTH",
       MAX(CASE WHEN month='12' THEN TIKTOK_Growth ELSE 0 END) AS "M12_TIKTOK_POSTS_GROWTH",
       MAX(CASE WHEN month='1' THEN AIRPLAY_Growth ELSE 0 END) AS "M1_AIRPLAY_STREAMS_GROWTH",
       MAX(CASE WHEN month='2' THEN AIRPLAY_Growth ELSE 0 END) AS "M2_AIRPLAY_STREAMS_GROWTH",
       MAX(CASE WHEN month='3' THEN AIRPLAY_Growth ELSE 0 END) AS "M3_AIRPLAY_STREAMS_GROWTH",
       MAX(CASE WHEN month='4' THEN AIRPLAY_Growth ELSE 0 END) AS "M4_AIRPLAY_STREAMS_GROWTH",
       MAX(CASE WHEN month='5' THEN AIRPLAY_Growth ELSE 0 END) AS "M5_AIRPLAY_STREAMS_GROWTH",
       MAX(CASE WHEN month='6' THEN AIRPLAY_Growth ELSE 0 END) AS "M6_AIRPLAY_STREAMS_GROWTH",
       MAX(CASE WHEN month='7' THEN AIRPLAY_Growth ELSE 0 END) AS "M7_AIRPLAY_STREAMS_GROWTH",
       MAX(CASE WHEN month='8' THEN AIRPLAY_Growth ELSE 0 END) AS "M8_AIRPLAY_STREAMS_GROWTH",
       MAX(CASE WHEN month='9' THEN AIRPLAY_Growth ELSE 0 END) AS "M9_AIRPLAY_STREAMS_GROWTH",
       MAX(CASE WHEN month='10' THEN AIRPLAY_Growth ELSE 0 END) AS "M10_AIRPLAY_STREAMS_GROWTH",
       MAX(CASE WHEN month='11' THEN AIRPLAY_Growth ELSE 0 END) AS "M11_AIRPLAY_STREAMS_GROWTH",
       MAX(CASE WHEN month='12' THEN AIRPLAY_Growth ELSE 0 END) AS "M12_AIRPLAY_STREAMS_GROWTH"      
FROM Growth_Rate
GROUP BY 1,2),

WOW_FINAL AS (
SELECT CM_TRACK, SUM(A) AS WK1_YT_VIEWS_GROWTH, SUM(B) AS WK2_YT_VIEWS_GROWTH, SUM(C) AS WK3_YT_VIEWS_GROWTH,
       SUM(D) AS WK4_YT_VIEWS_GROWTH, SUM(E) AS WK5_YT_VIEWS_GROWTH, SUM(F) AS WK6_YT_VIEWS_GROWTH,
       SUM(G) AS WK7_YT_VIEWS_GROWTH, SUM(H) AS WK8_YT_VIEWS_GROWTH, SUM(I) AS WK9_YT_VIEWS_GROWTH,
       SUM(J) AS WK10_YT_VIEWS_GROWTH, SUM(K) AS WK11_YT_VIEWS_GROWTH, SUM(L) AS WK12_YT_VIEWS_GROWTH,
       SUM(M) AS WK13_YT_VIEWS_GROWTH, SUM(N) AS WK14_YT_VIEWS_GROWTH, SUM(O) AS WK15_YT_VIEWS_GROWTH, -- END OF YT 
       SUM(P) AS WK1_SPOTIFY_PLAYS_GROWTH, SUM(Q) AS WK2_SPOTIFY_PLAYS_GROWTH, SUM(R) AS WK3_SPOTIFY_PLAYS_GROWTH,
       SUM(S) AS WK4_SPOTIFY_PLAYS_GROWTH, SUM(T) AS WK5_SPOTIFY_PLAYS_GROWTH, SUM(U) AS WK6_SPOTIFY_PLAYS_GROWTH,
       SUM(V) AS WK7_SPOTIFY_PLAYS_GROWTH, SUM(W) AS WK8_SPOTIFY_PLAYS_GROWTH, SUM(X) AS WK9_SPOTIFY_PLAYS_GROWTH,
       SUM(Y) AS WK10_SPOTIFY_PLAYS_GROWTH, SUM(Z) AS WK11_SPOTIFY_PLAYS_GROWTH, SUM(AA) AS WK12_SPOTIFY_PLAYS_GROWTH,
       SUM(BB) AS WK13_SPOTIFY_PLAYS_GROWTH, SUM(CC) AS WK14_SPOTIFY_PLAYS_GROWTH, SUM(DD) AS WK15_SPOTIFY_PLAYS_GROWTH, -- END OF SPOTIFY
       SUM(EE) AS WK1_TIKTOK_POSTS_GROWTH, SUM(FF) AS WK2_TIKTOK_POSTS_GROWTH, SUM(GG) AS WK3_TIKTOK_POSTS_GROWTH,
       SUM(HH) AS WK4_TIKTOK_POSTS_GROWTH, SUM(II) AS WK5_TIKTOK_POSTS_GROWTH, SUM(JJ) AS WK6_TIKTOK_POSTS_GROWTH,
       SUM(KK) AS WK7_TIKTOK_POSTS_GROWTH, SUM(LL) AS WK8_TIKTOK_POSTS_GROWTH, SUM(MM) AS WK9_TIKTOK_POSTS_GROWTH,
       SUM(NN) AS WK10_TIKTOK_POSTS_GROWTH, SUM(OO) AS WK11_TIKTOK_POSTS_GROWTH, SUM(PP) AS WK12_TIKTOK_POSTS_GROWTH,
       SUM(QQ) AS WK13_TIKTOK_POSTS_GROWTH, SUM(RR) AS WK14_TIKTOK_POSTS_GROWTH, SUM(SS) AS WK15_TIKTOK_POSTS_GROWTH
FROM(
     SELECT CM_TRACK, CREATED_AT, 
            SUM(CASE WHEN YEAR_WEEK = 1 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS A,SUM(CASE WHEN YEAR_WEEK = 2 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS B,
            SUM(CASE WHEN YEAR_WEEK = 3 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS C,SUM(CASE WHEN YEAR_WEEK = 4 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS D,
            SUM(CASE WHEN YEAR_WEEK = 5 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS E,SUM(CASE WHEN YEAR_WEEK = 6 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS F,
            SUM(CASE WHEN YEAR_WEEK = 7 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS G,SUM(CASE WHEN YEAR_WEEK = 8 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS H,
            SUM(CASE WHEN YEAR_WEEK = 9 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS I,SUM(CASE WHEN YEAR_WEEK = 10 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS J,
            SUM(CASE WHEN YEAR_WEEK = 11 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS K,SUM(CASE WHEN YEAR_WEEK = 12 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS L,
            SUM(CASE WHEN YEAR_WEEK = 13 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS M,SUM(CASE WHEN YEAR_WEEK = 14 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS N,
            SUM(CASE WHEN YEAR_WEEK = 15 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS O,SUM(CASE WHEN YEAR_WEEK = 1 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS P, -- END OF YT
            SUM(CASE WHEN YEAR_WEEK = 2 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS Q,SUM(CASE WHEN YEAR_WEEK = 3 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS R,
            SUM(CASE WHEN YEAR_WEEK = 4 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS S,SUM(CASE WHEN YEAR_WEEK = 5 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS T,
            SUM(CASE WHEN YEAR_WEEK = 6 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS U,SUM(CASE WHEN YEAR_WEEK = 7 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS V,
            SUM(CASE WHEN YEAR_WEEK = 8 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS W,SUM(CASE WHEN YEAR_WEEK = 9 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS X,
            SUM(CASE WHEN YEAR_WEEK = 10 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS Y,SUM(CASE WHEN YEAR_WEEK = 11 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS Z,
            SUM(CASE WHEN YEAR_WEEK = 12 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS AA,SUM(CASE WHEN YEAR_WEEK = 13 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS BB,
            SUM(CASE WHEN YEAR_WEEK = 14 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS CC,SUM(CASE WHEN YEAR_WEEK = 15 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS DD,  -- END OF SPOTIFY
            SUM(CASE WHEN YEAR_WEEK = 1 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS EE,
            SUM(CASE WHEN YEAR_WEEK = 2 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS FF,
            SUM(CASE WHEN YEAR_WEEK = 3 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS GG,
            SUM(CASE WHEN YEAR_WEEK = 4 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS HH,
            SUM(CASE WHEN YEAR_WEEK = 5 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS II,
            SUM(CASE WHEN YEAR_WEEK = 6 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS JJ,
            SUM(CASE WHEN YEAR_WEEK = 7 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS KK,
            SUM(CASE WHEN YEAR_WEEK = 8 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS LL,
            SUM(CASE WHEN YEAR_WEEK = 9 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS MM,
            SUM(CASE WHEN YEAR_WEEK = 10 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS NN,
            SUM(CASE WHEN YEAR_WEEK = 11 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS OO,
            SUM(CASE WHEN YEAR_WEEK = 12 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS PP,
            SUM(CASE WHEN YEAR_WEEK = 13 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS QQ,
            SUM(CASE WHEN YEAR_WEEK = 14 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS RR,
            SUM(CASE WHEN YEAR_WEEK = 15 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS SS
     FROM (
           SELECT * 
           FROM (
                 SELECT CM_TRACK, CREATED_AT, SUM(YOUTUBE_VIEWS) AS YT_VIEWS,
                 SUM(YOUTUBE_VIEWS) - LAG(SUM(YOUTUBE_VIEWS), 7) OVER (ORDER BY CREATED_AT) AS WOW_YT_VIEWS_GROWTH,
                 SUM(SPOTIFY_PLAYS) - LAG(SUM(SPOTIFY_PLAYS), 7) OVER (ORDER BY CREATED_AT) AS WOW_SPOTIFY_PLAYS_GROWTH,
                 SUM(TIKTOK_POSTS) - LAG(SUM(TIKTOK_POSTS), 7) OVER (ORDER BY CREATED_AT) AS WOW_TIKTOK_POSTS_GROWTH,                  
                 1 + (CREATED_AT - DATE_TRUNC('year', CREATED_AT)) / 7 as YEAR_WEEK
                 FROM "CHARTMETRIC"."SHARES"."CAPSTONE_TRACK_STATS" AS TS
                 WHERE CREATED_AT BETWEEN '2021-01-01' AND '2021-12-31'
                 GROUP BY CREATED_AT, CM_TRACK
                 ORDER BY 2
                )       
           WHERE YEAR_WEEK = ROUND(YEAR_WEEK, 0))
       GROUP BY CM_TRACK, CREATED_AT
       ORDER BY CREATED_AT)
GROUP BY CM_TRACK
)

SELECT *
FROM BASE
LEFT JOIN QUARTERS_FINAL USING (CM_TRACK)
LEFT JOIN MOM_FINAL USING(CM_TRACK)
LEFT JOIN WOW_FINAL USING(CM_TRACK)

"""
In [5]:
df=pd.read_sql(q1, conn)
df
/Users/angelikalin/opt/anaconda3/lib/python3.9/site-packages/pandas/io/sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy
  warnings.warn(
Out[5]:
CM_TRACK TRACK_NAME CM_ALBUM ALBUM_NAME RELEASE_DATE ARTIST_NAME ARTIST_COUNTRY_CODE2 COUNTRY_NAME GENRE_ID ARTIST_GENRE ... WK6_TIKTOK_POSTS_GROWTH WK7_TIKTOK_POSTS_GROWTH WK8_TIKTOK_POSTS_GROWTH WK9_TIKTOK_POSTS_GROWTH WK10_TIKTOK_POSTS_GROWTH WK11_TIKTOK_POSTS_GROWTH WK12_TIKTOK_POSTS_GROWTH WK13_TIKTOK_POSTS_GROWTH WK14_TIKTOK_POSTS_GROWTH WK15_TIKTOK_POSTS_GROWTH
0 11019719 Keep On Running 12498342 Sea of Approval (Deluxe) 2014-01-01 Andy Bull AU Australia 462887.0 Electronic ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1 16011936 Willing And Able 1106967 Diamonds And Pearls 1991-01-01 Prince US United States 462884.0 Rock ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2 11162739 Les Derniers Seront Les Premiers 711236 Original Album Classics 1990-01-01 Céline Dion CA Canada 462882.0 Pop ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3 60414497 Calima 14685962 Calima - Single 2021-09-16 None None None NaN None ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4 64718251 Symphony No. 39 in E-Flat Major, K. 543: IV. F... 15415581 Mozart: Symphonies Nos. 38 & 39 2021-10-22 NDR Radiophilharmonie US United States 462882.0 Pop ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
102540 34485129 Sad Song 8173893 Sad Song 2021-04-14 Hope Waidley US United States 462882.0 Pop ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
102541 15813972 Valentine's Day 889361 Tunnel Of Love 1987-09-28 Bruce Springsteen US United States 462891.0 Singer/Songwriter ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
102542 66304387 I Deserve (w/ NOS.) 18951649 Black Love Is... 2022-02-10 Smino US United States 462883.0 Hip-Hop/Rap ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
102543 32822574 We Were Right 7475728 We Were Right 2021-02-12 AKA AKA DE Germany 462887.0 Electronic ... 0.0 0.0 -703.0 0.0 -535892.0 5.0 -1183.0 0.0 0.0 -649.0
102544 17119125 I'll Find My Way Home - Remastered 6939072 Christmas Chill 2020-10-30 Jon & Vangelis GR Greece 462882.0 Pop ... 0.0 0.0 0.0 0.0 0.0 -245.0 0.0 0.0 -18.0 -254.0

102545 rows × 153 columns

Data Preparation¶

Using all songs crashed my kernel¶

In [6]:
### Ramdomly sample out 50k songs 
df_sample = df.sample(n=50000, random_state=123)
df_sample 
Out[6]:
CM_TRACK TRACK_NAME CM_ALBUM ALBUM_NAME RELEASE_DATE ARTIST_NAME ARTIST_COUNTRY_CODE2 COUNTRY_NAME GENRE_ID ARTIST_GENRE ... WK6_TIKTOK_POSTS_GROWTH WK7_TIKTOK_POSTS_GROWTH WK8_TIKTOK_POSTS_GROWTH WK9_TIKTOK_POSTS_GROWTH WK10_TIKTOK_POSTS_GROWTH WK11_TIKTOK_POSTS_GROWTH WK12_TIKTOK_POSTS_GROWTH WK13_TIKTOK_POSTS_GROWTH WK14_TIKTOK_POSTS_GROWTH WK15_TIKTOK_POSTS_GROWTH
63119 28343556 Te Prefiero Lejos 5653976 Te Prefiero Lejos 2020-01-31 Ilegales ES Spain 462929.0 Punk ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
35359 15464089 Pour Out A Little Liquor 2939501 Wesside Classic, Vol. 1 2014-07-29 2Pac US United States 462883.0 Hip-Hop/Rap ... 0.0 -1793.0 162.0 0.0 136.0 177.0 0.0 0.0 0.0 0.0
46459 65313103 Pages (feat. Rook) 19843874 Last Day On Earth 2022-04-08 SK8 US United States 462882.0 Pop ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
99290 12307342 Ma religion dans son regard 3435571 L'album de sa vie 50 titres 2018-09-28 Johnny Hallyday FR France 462882.0 Pop ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -23.0 0.0 0.0
295 48212874 QUEEN 11336333 FEMULINE 2021-06-08 Todrick Hall US United States 462883.0 Hip-Hop/Rap ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
6343 11364916 Kapitel 14 - Der Weihnachtsabend (Folge 051) 1553845 Folge 51: Der Weihnachtsabend 1986-01-01 Benjamin BlĂŒmchen DE Germany 462932.0 Spoken Word ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
73522 11328739 Hei, Pippi Langstrumpf 224606 The Best of Generation Fernseh-Kult 2010-01-05 Pippi Langstrumpf DE Germany 462885.0 Dance ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
94075 20016819 Will You Sing 2650049 Heaven and Earth 2018-06-22 Kamasi Washington US United States 462893.0 Jazz ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
58013 15483779 I Belong To You 6159205 You Mean the World to Me: The Best Love Songs ... 2020-06-12 Toni Braxton US United States 462889.0 R&B/Soul ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -5535.0 0.0 -24349.0
18879 15782231 The Wexford Carol 63071 Songs of Joy & Peace 2008-10-10 Natalie MacMaster CA Canada 462903.0 Holiday ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

50000 rows × 153 columns

In [7]:
### Create function to replace COUNTRY_NAME with COUNTRY_ID 

def country_identity(df):
    
    country = df['COUNTRY_NAME'].to_numpy()
    countries_list = []
    
    for x in country:
        if x not in countries_list:
            countries_list.append(x)
    
    #Create id starting at 1000
    df_country_name = df['COUNTRY_NAME']
    df_country_id = np.arange(1000, 1000 + len(df.COUNTRY_NAME.unique()),1)
    #replace 
    df['COUNTRY_NAME'].replace(countries_list, df_country_id, inplace = True)
    return(df)

country_identity(df_sample)
Out[7]:
CM_TRACK TRACK_NAME CM_ALBUM ALBUM_NAME RELEASE_DATE ARTIST_NAME ARTIST_COUNTRY_CODE2 COUNTRY_NAME GENRE_ID ARTIST_GENRE ... WK6_TIKTOK_POSTS_GROWTH WK7_TIKTOK_POSTS_GROWTH WK8_TIKTOK_POSTS_GROWTH WK9_TIKTOK_POSTS_GROWTH WK10_TIKTOK_POSTS_GROWTH WK11_TIKTOK_POSTS_GROWTH WK12_TIKTOK_POSTS_GROWTH WK13_TIKTOK_POSTS_GROWTH WK14_TIKTOK_POSTS_GROWTH WK15_TIKTOK_POSTS_GROWTH
63119 28343556 Te Prefiero Lejos 5653976 Te Prefiero Lejos 2020-01-31 Ilegales ES 1000 462929.0 Punk ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
35359 15464089 Pour Out A Little Liquor 2939501 Wesside Classic, Vol. 1 2014-07-29 2Pac US 1001 462883.0 Hip-Hop/Rap ... 0.0 -1793.0 162.0 0.0 136.0 177.0 0.0 0.0 0.0 0.0
46459 65313103 Pages (feat. Rook) 19843874 Last Day On Earth 2022-04-08 SK8 US 1001 462882.0 Pop ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
99290 12307342 Ma religion dans son regard 3435571 L'album de sa vie 50 titres 2018-09-28 Johnny Hallyday FR 1002 462882.0 Pop ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -23.0 0.0 0.0
295 48212874 QUEEN 11336333 FEMULINE 2021-06-08 Todrick Hall US 1001 462883.0 Hip-Hop/Rap ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
6343 11364916 Kapitel 14 - Der Weihnachtsabend (Folge 051) 1553845 Folge 51: Der Weihnachtsabend 1986-01-01 Benjamin BlĂŒmchen DE 1018 462932.0 Spoken Word ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
73522 11328739 Hei, Pippi Langstrumpf 224606 The Best of Generation Fernseh-Kult 2010-01-05 Pippi Langstrumpf DE 1018 462885.0 Dance ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
94075 20016819 Will You Sing 2650049 Heaven and Earth 2018-06-22 Kamasi Washington US 1001 462893.0 Jazz ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
58013 15483779 I Belong To You 6159205 You Mean the World to Me: The Best Love Songs ... 2020-06-12 Toni Braxton US 1001 462889.0 R&B/Soul ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -5535.0 0.0 -24349.0
18879 15782231 The Wexford Carol 63071 Songs of Joy & Peace 2008-10-10 Natalie MacMaster CA 1014 462903.0 Holiday ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

50000 rows × 153 columns

In [8]:
### Reduce cardinality for genre 

from collections import Counter
def cumulatively_categorise(column,threshold=0.75,return_categories_list=True):
  #Find the threshold value using the percentage and number of instances in the column
  threshold_value=int(threshold*len(column))
  #Initialise an empty list for our new minimised categories
  categories_list=[]
  #Initialise a variable to calculate the sum of frequencies
  s=0
  #Create a counter dictionary of the form unique_value: frequency
  counts=Counter(column)

  #Loop through the category name and its corresponding frequency after sorting the categories by descending order of frequency
  for i,j in counts.most_common():
    #Add the frequency to the global sum
    s+=dict(counts)[i]
    #Append the category name to the list
    categories_list.append(i)
    #Check if the global sum has reached the threshold value, if so break the loop
    if s>=threshold_value:
      break
  #Append the category Other to the list
  categories_list.append('Other')

  #Replace all instances not in our new categories by Other  
  new_column=column.apply(lambda x: x if x in categories_list else 'Other')

  #Return transformed column and unique values if return_categories=True
  if(return_categories_list):
    return new_column,categories_list
  #Return only the transformed column if return_categories=False
  else:
    return new_column


#Call the function with a default threshold of 75%
transformed_column,new_category_list=cumulatively_categorise(df_sample['ARTIST_GENRE'],return_categories_list=True)

df_sample['genre_new'] = transformed_column
df_sample.genre_new.value_counts()
Out[8]:
Other          11611
Pop             7733
Rock            5377
Hip-Hop/Rap     5187
Latin           2524
Electronic      2299
Alternative     2270
R&B/Soul        2201
Dance           1503
J-Pop           1481
Country         1443
Jazz            1236
House           1123
Latin Pop       1110
Name: genre_new, dtype: int64
In [9]:
### Hot encode Genre 
def its_hot(df):
    genre_dummies = pd.get_dummies(df.genre_new)
    df = pd.concat([df, genre_dummies], axis=1)
    return df 

df_sample = its_hot(df_sample)
In [11]:
pd.set_option('display.max_columns', None)
df_sample
Out[11]:
CM_TRACK TRACK_NAME CM_ALBUM ALBUM_NAME RELEASE_DATE ARTIST_NAME ARTIST_COUNTRY_CODE2 COUNTRY_NAME GENRE_ID ARTIST_GENRE DUPE Q1_YOUTUBE_VIEWS_GROWTH Q1_YOUTUBE_LIKES_GROWTH Q1_SPOTIFY_PLAYS_GROWTH Q1_SPOTIFY_POPULARITY_GROWTH Q1_TIKTOK_POSTS_GROWTH Q1_AIRPLAY_STREAMS_GROWTH Q2_YOUTUBE_VIEWS_GROWTH Q2_YOUTUBE_LIKES_GROWTH Q2_SPOTIFY_PLAYS_GROWTH Q2_SPOTIFY_POPULARITY_GROWTH Q2_TIKTOK_POSTS_GROWTH Q2_AIRPLAY_STREAMS_GROWTH Q3_YOUTUBE_VIEWS_GROWTH Q3_YOUTUBE_LIKES_GROWTH Q3_SPOTIFY_PLAYS_GROWTH Q3_SPOTIFY_POPULARITY_GROWTH Q3_TIKTOK_POSTS_GROWTH Q3_AIRPLAY_STREAMS_GROWTH Q4_YOUTUBE_VIEWS_GROWTH Q4_YOUTUBE_LIKES_GROWTH Q4_SPOTIFY_PLAYS_GROWTH Q4_SPOTIFY_POPULARITY_GROWTH Q4_TIKTOK_POSTS_GROWTH Q4_AIRPLAY_STREAMS_GROWTH GENRE M1_YOUTUBE_VIEWS_GROWTH M2_YOUTUBE_VIEWS_GROWTH M3_YOUTUBE_VIEWS_GROWTH M4_YOUTUBE_VIEWS_GROWTH M5_YOUTUBE_VIEWS_GROWTH M6_YOUTUBE_VIEWS_GROWTH M7_YOUTUBE_VIEWS_GROWTH M8_YOUTUBE_VIEWS_GROWTH M9_YOUTUBE_VIEWS_GROWTH M10_YOUTUBE_VIEWS_GROWTH M11_YOUTUBE_VIEWS_GROWTH M12_YOUTUBE_VIEWS_GROWTH M1_YOUTUBE_LIKES_GROWTH M2_YOUTUBE_LIKES_GROWTH M3_YOUTUBE_LIKES_GROWTH M4_YOUTUBE_LIKES_GROWTH M5_YOUTUBE_LIKES_GROWTH M6_YOUTUBE_LIKES_GROWTH M7_YOUTUBE_LIKES_GROWTH M8_YOUTUBE_LIKES_GROWTH M9_YOUTUBE_LIKES_GROWTH M10_YOUTUBE_LIKES_GROWTH M11_YOUTUBE_LIKES_GROWTH M12_YOUTUBE_LIKES_GROWTH M1_SPOTIFY_PLAYS_GROWTH M2_SPOTIFY_PLAYS_GROWTH M3_SPOTIFY_PLAYS_GROWTH M4_SPOTIFY_PLAYS_GROWTH M5_SPOTIFY_PLAYS_GROWTH M6_SPOTIFY_PLAYS_GROWTH M7_SPOTIFY_PLAYS_GROWTH M8_SPOTIFY_PLAYS_GROWTH M9_SPOTIFY_PLAYS_GROWTH M10_SPOTIFY_PLAYS_GROWTH M11_SPOTIFY_PLAYS_GROWTH M12_SPOTIFY_PLAYS_GROWTH M1_SPOTIFY_POPULARITY_GROWTH M2_SPOTIFY_POPULARITY_GROWTH M3_SPOTIFY_POPULARITY_GROWTH M4_SPOTIFY_POPULARITY_GROWTH M5_SPOTIFY_POPULARITY_GROWTH M6_SPOTIFY_POPULARITY_GROWTH M7_SPOTIFY_POPULARITY_GROWTH M8_SPOTIFY_POPULARITY_GROWTH M9_SPOTIFY_POPULARITY_GROWTH M10_SPOTIFY_POPULARITY_GROWTH M11_SPOTIFY_POPULARITY_GROWTH M12_SPOTIFY_POPULARITY_GROWTH M1_TIKTOK_POSTS_GROWTH M2_TIKTOK_POSTS_GROWTH M3_TIKTOK_POSTS_GROWTH M4_TIKTOK_POSTS_GROWTH M5_TIKTOK_POSTS_GROWTH M6_TIKTOK_POSTS_GROWTH M7_TIKTOK_POSTS_GROWTH M8_TIKTOK_POSTS_GROWTH M9_TIKTOK_POSTS_GROWTH M10_TIKTOK_POSTS_GROWTH M11_TIKTOK_POSTS_GROWTH M12_TIKTOK_POSTS_GROWTH M1_AIRPLAY_STREAMS_GROWTH M2_AIRPLAY_STREAMS_GROWTH M3_AIRPLAY_STREAMS_GROWTH M4_AIRPLAY_STREAMS_GROWTH M5_AIRPLAY_STREAMS_GROWTH M6_AIRPLAY_STREAMS_GROWTH M7_AIRPLAY_STREAMS_GROWTH M8_AIRPLAY_STREAMS_GROWTH M9_AIRPLAY_STREAMS_GROWTH M10_AIRPLAY_STREAMS_GROWTH M11_AIRPLAY_STREAMS_GROWTH M12_AIRPLAY_STREAMS_GROWTH WK1_YT_VIEWS_GROWTH WK2_YT_VIEWS_GROWTH WK3_YT_VIEWS_GROWTH WK4_YT_VIEWS_GROWTH WK5_YT_VIEWS_GROWTH WK6_YT_VIEWS_GROWTH WK7_YT_VIEWS_GROWTH WK8_YT_VIEWS_GROWTH WK9_YT_VIEWS_GROWTH WK10_YT_VIEWS_GROWTH WK11_YT_VIEWS_GROWTH WK12_YT_VIEWS_GROWTH WK13_YT_VIEWS_GROWTH WK14_YT_VIEWS_GROWTH WK15_YT_VIEWS_GROWTH WK1_SPOTIFY_PLAYS_GROWTH WK2_SPOTIFY_PLAYS_GROWTH WK3_SPOTIFY_PLAYS_GROWTH WK4_SPOTIFY_PLAYS_GROWTH WK5_SPOTIFY_PLAYS_GROWTH WK6_SPOTIFY_PLAYS_GROWTH WK7_SPOTIFY_PLAYS_GROWTH WK8_SPOTIFY_PLAYS_GROWTH WK9_SPOTIFY_PLAYS_GROWTH WK10_SPOTIFY_PLAYS_GROWTH WK11_SPOTIFY_PLAYS_GROWTH WK12_SPOTIFY_PLAYS_GROWTH WK13_SPOTIFY_PLAYS_GROWTH WK14_SPOTIFY_PLAYS_GROWTH WK15_SPOTIFY_PLAYS_GROWTH WK1_TIKTOK_POSTS_GROWTH WK2_TIKTOK_POSTS_GROWTH WK3_TIKTOK_POSTS_GROWTH WK4_TIKTOK_POSTS_GROWTH WK5_TIKTOK_POSTS_GROWTH WK6_TIKTOK_POSTS_GROWTH WK7_TIKTOK_POSTS_GROWTH WK8_TIKTOK_POSTS_GROWTH WK9_TIKTOK_POSTS_GROWTH WK10_TIKTOK_POSTS_GROWTH WK11_TIKTOK_POSTS_GROWTH WK12_TIKTOK_POSTS_GROWTH WK13_TIKTOK_POSTS_GROWTH WK14_TIKTOK_POSTS_GROWTH WK15_TIKTOK_POSTS_GROWTH genre_new Alternative Country Dance Electronic Hip-Hop/Rap House J-Pop Jazz Latin Latin Pop Other Pop R&B/Soul Rock
63119 28343556 Te Prefiero Lejos 5653976 Te Prefiero Lejos 2020-01-31 Ilegales ES 1000 462929.0 Punk 1 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 -6.0 0.0 0.0 0.0 0.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 5.0 0.0 0.0 Latin 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Other 0 0 0 0 0 0 0 0 0 0 1 0 0 0
35359 15464089 Pour Out A Little Liquor 2939501 Wesside Classic, Vol. 1 2014-07-29 2Pac US 1001 462883.0 Hip-Hop/Rap 1 0.0 0.0 0.0 49.0 25.0 1.0 0.0 0.0 0.0 0.0 10.0 0.0 0.0 0.0 456925.0 -35.0 0.0 0.0 0.0 0.0 512246.0 37.0 0.0 0.0 Hip-Hop/Rap 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 155876.0 145978.0 162369.0 148578.0 166433.0 161453.0 184360.0 0.0 0.0 2.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 35.0 1.0 0.0 17.0 5.0 13.0 0.0 0.0 14.0 16.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1793.0 162.0 0.0 136.0 177.0 0.0 0.0 0.0 0.0 Hip-Hop/Rap 0 0 0 0 1 0 0 0 0 0 0 0 0 0
46459 65313103 Pages (feat. Rook) 19843874 Last Day On Earth 2022-04-08 SK8 US 1001 462882.0 Pop 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Dance 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 17506.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Pop 0 0 0 0 0 0 0 0 0 0 0 1 0 0
99290 12307342 Ma religion dans son regard 3435571 L'album de sa vie 50 titres 2018-09-28 Johnny Hallyday FR 1002 462882.0 Pop 1 0.0 0.0 0.0 38.0 16.0 10.0 0.0 0.0 0.0 -2.0 20.0 12.0 0.0 0.0 0.0 -23.0 6.0 9.0 0.0 0.0 0.0 3.0 68.0 13.0 Pop 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 13.0 0.0 7.0 1.0 0.0 0.0 0.0 1.0 11.0 8.0 0.0 8.0 0.0 46.0 0.0 0.0 0.0 1.0 4.0 3.0 4.0 5.0 4.0 2.0 3.0 4.0 2.0 7.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -23.0 0.0 0.0 Pop 0 0 0 0 0 0 0 0 0 0 0 1 0 0
295 48212874 QUEEN 11336333 FEMULINE 2021-06-08 Todrick Hall US 1001 462883.0 Hip-Hop/Rap 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -53.0 0.0 1.0 0.0 0.0 0.0 -1.0 0.0 0.0 Dance 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 45.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 124.0 25.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hip-Hop/Rap 0 0 0 0 1 0 0 0 0 0 0 0 0 0
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6343 11364916 Kapitel 14 - Der Weihnachtsabend (Folge 051) 1553845 Folge 51: Der Weihnachtsabend 1986-01-01 Benjamin BlĂŒmchen DE 1018 462932.0 Spoken Word 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -3.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Children's Music 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 2.0 3.0 5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Other 0 0 0 0 0 0 0 0 0 0 1 0 0 0
73522 11328739 Hei, Pippi Langstrumpf 224606 The Best of Generation Fernseh-Kult 2010-01-05 Pippi Langstrumpf DE 1018 462885.0 Dance 1 0.0 0.0 0.0 0.0 0.0 4.0 0.0 0.0 0.0 -6.0 0.0 17.0 0.0 0.0 0.0 0.0 0.0 29.0 0.0 0.0 290200.0 0.0 0.0 8.0 Dance 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 79860.0 94450.0 113454.0 95626.0 81120.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.0 2.0 4.0 11.0 9.0 2.0 18.0 2.0 2.0 4.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Dance 0 0 1 0 0 0 0 0 0 0 0 0 0 0
94075 20016819 Will You Sing 2650049 Heaven and Earth 2018-06-22 Kamasi Washington US 1001 462893.0 Jazz 1 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 21.0 0.0 0.0 0.0 0.0 0.0 -2.0 0.0 1.0 0.0 0.0 0.0 -1.0 0.0 0.0 Jazz 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 18.0 0.0 3.0 0.0 19.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Jazz 0 0 0 0 0 0 0 1 0 0 0 0 0 0
58013 15483779 I Belong To You 6159205 You Mean the World to Me: The Best Love Songs ... 2020-06-12 Toni Braxton US 1001 462889.0 R&B/Soul 1 0.0 0.0 0.0 30.0 0.0 18.0 0.0 0.0 0.0 1.0 12.0 12.0 0.0 0.0 0.0 -31.0 10.0 6.0 0.0 0.0 0.0 3.0 17.0 10.0 Dance 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 28.0 0.0 0.0 2.0 1.0 0.0 5.0 0.0 0.0 0.0 6.0 5.0 1.0 0.0 0.0 13.0 4.0 0.0 84.0 0.0 3.0 4.0 3.0 5.0 4.0 1.0 5.0 0.0 4.0 5.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -5535.0 0.0 -24349.0 R&B/Soul 0 0 0 0 0 0 0 0 0 0 0 0 1 0
18879 15782231 The Wexford Carol 63071 Songs of Joy & Peace 2008-10-10 Natalie MacMaster CA 1014 462903.0 Holiday 1 4452837.0 242.0 0.0 0.0 0.0 0.0 17360.0 99.0 0.0 -2.0 0.0 1.0 17681.0 149.0 0.0 -2.0 0.0 0.0 332171.0 5727.0 0.0 -14.0 0.0 37.0 Bluegrass 0.0 11908.0 9393.0 6273.0 6181.0 4906.0 4981.0 6277.0 6423.0 10964.0 22193.0 299014.0 0.0 51.0 59.0 33.0 36.0 30.0 36.0 51.0 62.0 96.0 281.0 5350.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 139654.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 14.0 23.0 0.0 0.0 2767948.0 3637632.0 0.0 0.0 0.0 0.0 0.0 4358538.0 0.0 2977198.0 0.0 0.0 2086151.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Other 0 0 0 0 0 0 0 0 0 0 1 0 0 0

50000 rows × 168 columns

In [12]:
### Remove generic columns 
df_sample=df_sample.loc[:, df_sample.columns.drop(['CM_TRACK','CM_ALBUM','ALBUM_NAME','RELEASE_DATE',
                                            'ARTIST_NAME', 'ARTIST_COUNTRY_CODE2', 'GENRE_ID',
                                              'ARTIST_GENRE' ,'DUPE', 'GENRE', 'genre_new'])]
In [13]:
### Remove any NaNs 

def clean_dataset(df):
    df = df.set_index('TRACK_NAME')
    assert isinstance(df, pd.DataFrame), "df needs to be a pd.DataFrame"
    df.dropna(inplace=True)
    indices_to_keep = ~df.isin([np.nan, np.inf, -np.inf]).any(1)
    return df[indices_to_keep].astype(np.float64)

df_sample = clean_dataset(df_sample)
In [14]:
### Scale data 
#df_sample = df_sample.set_index('TRACK_NAME')
from sklearn.preprocessing import StandardScaler #used for 'Feature Scaling'
scaler = StandardScaler()
df_sample_scaled = pd.DataFrame(scaler.fit_transform(df_sample), index=df_sample.index, columns=df_sample.columns)
display('original',df_sample.head(2),'scaled',df_sample_scaled.head(2))
'original'
COUNTRY_NAME Q1_YOUTUBE_VIEWS_GROWTH Q1_YOUTUBE_LIKES_GROWTH Q1_SPOTIFY_PLAYS_GROWTH Q1_SPOTIFY_POPULARITY_GROWTH Q1_TIKTOK_POSTS_GROWTH Q1_AIRPLAY_STREAMS_GROWTH Q2_YOUTUBE_VIEWS_GROWTH Q2_YOUTUBE_LIKES_GROWTH Q2_SPOTIFY_PLAYS_GROWTH Q2_SPOTIFY_POPULARITY_GROWTH Q2_TIKTOK_POSTS_GROWTH Q2_AIRPLAY_STREAMS_GROWTH Q3_YOUTUBE_VIEWS_GROWTH Q3_YOUTUBE_LIKES_GROWTH Q3_SPOTIFY_PLAYS_GROWTH Q3_SPOTIFY_POPULARITY_GROWTH Q3_TIKTOK_POSTS_GROWTH Q3_AIRPLAY_STREAMS_GROWTH Q4_YOUTUBE_VIEWS_GROWTH Q4_YOUTUBE_LIKES_GROWTH Q4_SPOTIFY_PLAYS_GROWTH Q4_SPOTIFY_POPULARITY_GROWTH Q4_TIKTOK_POSTS_GROWTH Q4_AIRPLAY_STREAMS_GROWTH M1_YOUTUBE_VIEWS_GROWTH M2_YOUTUBE_VIEWS_GROWTH M3_YOUTUBE_VIEWS_GROWTH M4_YOUTUBE_VIEWS_GROWTH M5_YOUTUBE_VIEWS_GROWTH M6_YOUTUBE_VIEWS_GROWTH M7_YOUTUBE_VIEWS_GROWTH M8_YOUTUBE_VIEWS_GROWTH M9_YOUTUBE_VIEWS_GROWTH M10_YOUTUBE_VIEWS_GROWTH M11_YOUTUBE_VIEWS_GROWTH M12_YOUTUBE_VIEWS_GROWTH M1_YOUTUBE_LIKES_GROWTH M2_YOUTUBE_LIKES_GROWTH M3_YOUTUBE_LIKES_GROWTH M4_YOUTUBE_LIKES_GROWTH M5_YOUTUBE_LIKES_GROWTH M6_YOUTUBE_LIKES_GROWTH M7_YOUTUBE_LIKES_GROWTH M8_YOUTUBE_LIKES_GROWTH M9_YOUTUBE_LIKES_GROWTH M10_YOUTUBE_LIKES_GROWTH M11_YOUTUBE_LIKES_GROWTH M12_YOUTUBE_LIKES_GROWTH M1_SPOTIFY_PLAYS_GROWTH M2_SPOTIFY_PLAYS_GROWTH M3_SPOTIFY_PLAYS_GROWTH M4_SPOTIFY_PLAYS_GROWTH M5_SPOTIFY_PLAYS_GROWTH M6_SPOTIFY_PLAYS_GROWTH M7_SPOTIFY_PLAYS_GROWTH M8_SPOTIFY_PLAYS_GROWTH M9_SPOTIFY_PLAYS_GROWTH M10_SPOTIFY_PLAYS_GROWTH M11_SPOTIFY_PLAYS_GROWTH M12_SPOTIFY_PLAYS_GROWTH M1_SPOTIFY_POPULARITY_GROWTH M2_SPOTIFY_POPULARITY_GROWTH M3_SPOTIFY_POPULARITY_GROWTH M4_SPOTIFY_POPULARITY_GROWTH M5_SPOTIFY_POPULARITY_GROWTH M6_SPOTIFY_POPULARITY_GROWTH M7_SPOTIFY_POPULARITY_GROWTH M8_SPOTIFY_POPULARITY_GROWTH M9_SPOTIFY_POPULARITY_GROWTH M10_SPOTIFY_POPULARITY_GROWTH M11_SPOTIFY_POPULARITY_GROWTH M12_SPOTIFY_POPULARITY_GROWTH M1_TIKTOK_POSTS_GROWTH M2_TIKTOK_POSTS_GROWTH M3_TIKTOK_POSTS_GROWTH M4_TIKTOK_POSTS_GROWTH M5_TIKTOK_POSTS_GROWTH M6_TIKTOK_POSTS_GROWTH M7_TIKTOK_POSTS_GROWTH M8_TIKTOK_POSTS_GROWTH M9_TIKTOK_POSTS_GROWTH M10_TIKTOK_POSTS_GROWTH M11_TIKTOK_POSTS_GROWTH M12_TIKTOK_POSTS_GROWTH M1_AIRPLAY_STREAMS_GROWTH M2_AIRPLAY_STREAMS_GROWTH M3_AIRPLAY_STREAMS_GROWTH M4_AIRPLAY_STREAMS_GROWTH M5_AIRPLAY_STREAMS_GROWTH M6_AIRPLAY_STREAMS_GROWTH M7_AIRPLAY_STREAMS_GROWTH M8_AIRPLAY_STREAMS_GROWTH M9_AIRPLAY_STREAMS_GROWTH M10_AIRPLAY_STREAMS_GROWTH M11_AIRPLAY_STREAMS_GROWTH M12_AIRPLAY_STREAMS_GROWTH WK1_YT_VIEWS_GROWTH WK2_YT_VIEWS_GROWTH WK3_YT_VIEWS_GROWTH WK4_YT_VIEWS_GROWTH WK5_YT_VIEWS_GROWTH WK6_YT_VIEWS_GROWTH WK7_YT_VIEWS_GROWTH WK8_YT_VIEWS_GROWTH WK9_YT_VIEWS_GROWTH WK10_YT_VIEWS_GROWTH WK11_YT_VIEWS_GROWTH WK12_YT_VIEWS_GROWTH WK13_YT_VIEWS_GROWTH WK14_YT_VIEWS_GROWTH WK15_YT_VIEWS_GROWTH WK1_SPOTIFY_PLAYS_GROWTH WK2_SPOTIFY_PLAYS_GROWTH WK3_SPOTIFY_PLAYS_GROWTH WK4_SPOTIFY_PLAYS_GROWTH WK5_SPOTIFY_PLAYS_GROWTH WK6_SPOTIFY_PLAYS_GROWTH WK7_SPOTIFY_PLAYS_GROWTH WK8_SPOTIFY_PLAYS_GROWTH WK9_SPOTIFY_PLAYS_GROWTH WK10_SPOTIFY_PLAYS_GROWTH WK11_SPOTIFY_PLAYS_GROWTH WK12_SPOTIFY_PLAYS_GROWTH WK13_SPOTIFY_PLAYS_GROWTH WK14_SPOTIFY_PLAYS_GROWTH WK15_SPOTIFY_PLAYS_GROWTH WK1_TIKTOK_POSTS_GROWTH WK2_TIKTOK_POSTS_GROWTH WK3_TIKTOK_POSTS_GROWTH WK4_TIKTOK_POSTS_GROWTH WK5_TIKTOK_POSTS_GROWTH WK6_TIKTOK_POSTS_GROWTH WK7_TIKTOK_POSTS_GROWTH WK8_TIKTOK_POSTS_GROWTH WK9_TIKTOK_POSTS_GROWTH WK10_TIKTOK_POSTS_GROWTH WK11_TIKTOK_POSTS_GROWTH WK12_TIKTOK_POSTS_GROWTH WK13_TIKTOK_POSTS_GROWTH WK14_TIKTOK_POSTS_GROWTH WK15_TIKTOK_POSTS_GROWTH Alternative Country Dance Electronic Hip-Hop/Rap House J-Pop Jazz Latin Latin Pop Other Pop R&B/Soul Rock
TRACK_NAME
Te Prefiero Lejos 1000.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 -6.0 0.0 0.0 0.0 0.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0
Pour Out A Little Liquor 1001.0 0.0 0.0 0.0 49.0 25.0 1.0 0.0 0.0 0.0 0.0 10.0 0.0 0.0 0.0 456925.0 -35.0 0.0 0.0 0.0 0.0 512246.0 37.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 155876.0 145978.0 162369.0 148578.0 166433.0 161453.0 184360.0 0.0 0.0 2.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 35.0 1.0 0.0 17.0 5.0 13.0 0.0 0.0 14.0 16.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1793.0 162.0 0.0 136.0 177.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
'scaled'
COUNTRY_NAME Q1_YOUTUBE_VIEWS_GROWTH Q1_YOUTUBE_LIKES_GROWTH Q1_SPOTIFY_PLAYS_GROWTH Q1_SPOTIFY_POPULARITY_GROWTH Q1_TIKTOK_POSTS_GROWTH Q1_AIRPLAY_STREAMS_GROWTH Q2_YOUTUBE_VIEWS_GROWTH Q2_YOUTUBE_LIKES_GROWTH Q2_SPOTIFY_PLAYS_GROWTH Q2_SPOTIFY_POPULARITY_GROWTH Q2_TIKTOK_POSTS_GROWTH Q2_AIRPLAY_STREAMS_GROWTH Q3_YOUTUBE_VIEWS_GROWTH Q3_YOUTUBE_LIKES_GROWTH Q3_SPOTIFY_PLAYS_GROWTH Q3_SPOTIFY_POPULARITY_GROWTH Q3_TIKTOK_POSTS_GROWTH Q3_AIRPLAY_STREAMS_GROWTH Q4_YOUTUBE_VIEWS_GROWTH Q4_YOUTUBE_LIKES_GROWTH Q4_SPOTIFY_PLAYS_GROWTH Q4_SPOTIFY_POPULARITY_GROWTH Q4_TIKTOK_POSTS_GROWTH Q4_AIRPLAY_STREAMS_GROWTH M1_YOUTUBE_VIEWS_GROWTH M2_YOUTUBE_VIEWS_GROWTH M3_YOUTUBE_VIEWS_GROWTH M4_YOUTUBE_VIEWS_GROWTH M5_YOUTUBE_VIEWS_GROWTH M6_YOUTUBE_VIEWS_GROWTH M7_YOUTUBE_VIEWS_GROWTH M8_YOUTUBE_VIEWS_GROWTH M9_YOUTUBE_VIEWS_GROWTH M10_YOUTUBE_VIEWS_GROWTH M11_YOUTUBE_VIEWS_GROWTH M12_YOUTUBE_VIEWS_GROWTH M1_YOUTUBE_LIKES_GROWTH M2_YOUTUBE_LIKES_GROWTH M3_YOUTUBE_LIKES_GROWTH M4_YOUTUBE_LIKES_GROWTH M5_YOUTUBE_LIKES_GROWTH M6_YOUTUBE_LIKES_GROWTH M7_YOUTUBE_LIKES_GROWTH M8_YOUTUBE_LIKES_GROWTH M9_YOUTUBE_LIKES_GROWTH M10_YOUTUBE_LIKES_GROWTH M11_YOUTUBE_LIKES_GROWTH M12_YOUTUBE_LIKES_GROWTH M1_SPOTIFY_PLAYS_GROWTH M2_SPOTIFY_PLAYS_GROWTH M3_SPOTIFY_PLAYS_GROWTH M4_SPOTIFY_PLAYS_GROWTH M5_SPOTIFY_PLAYS_GROWTH M6_SPOTIFY_PLAYS_GROWTH M7_SPOTIFY_PLAYS_GROWTH M8_SPOTIFY_PLAYS_GROWTH M9_SPOTIFY_PLAYS_GROWTH M10_SPOTIFY_PLAYS_GROWTH M11_SPOTIFY_PLAYS_GROWTH M12_SPOTIFY_PLAYS_GROWTH M1_SPOTIFY_POPULARITY_GROWTH M2_SPOTIFY_POPULARITY_GROWTH M3_SPOTIFY_POPULARITY_GROWTH M4_SPOTIFY_POPULARITY_GROWTH M5_SPOTIFY_POPULARITY_GROWTH M6_SPOTIFY_POPULARITY_GROWTH M7_SPOTIFY_POPULARITY_GROWTH M8_SPOTIFY_POPULARITY_GROWTH M9_SPOTIFY_POPULARITY_GROWTH M10_SPOTIFY_POPULARITY_GROWTH M11_SPOTIFY_POPULARITY_GROWTH M12_SPOTIFY_POPULARITY_GROWTH M1_TIKTOK_POSTS_GROWTH M2_TIKTOK_POSTS_GROWTH M3_TIKTOK_POSTS_GROWTH M4_TIKTOK_POSTS_GROWTH M5_TIKTOK_POSTS_GROWTH M6_TIKTOK_POSTS_GROWTH M7_TIKTOK_POSTS_GROWTH M8_TIKTOK_POSTS_GROWTH M9_TIKTOK_POSTS_GROWTH M10_TIKTOK_POSTS_GROWTH M11_TIKTOK_POSTS_GROWTH M12_TIKTOK_POSTS_GROWTH M1_AIRPLAY_STREAMS_GROWTH M2_AIRPLAY_STREAMS_GROWTH M3_AIRPLAY_STREAMS_GROWTH M4_AIRPLAY_STREAMS_GROWTH M5_AIRPLAY_STREAMS_GROWTH M6_AIRPLAY_STREAMS_GROWTH M7_AIRPLAY_STREAMS_GROWTH M8_AIRPLAY_STREAMS_GROWTH M9_AIRPLAY_STREAMS_GROWTH M10_AIRPLAY_STREAMS_GROWTH M11_AIRPLAY_STREAMS_GROWTH M12_AIRPLAY_STREAMS_GROWTH WK1_YT_VIEWS_GROWTH WK2_YT_VIEWS_GROWTH WK3_YT_VIEWS_GROWTH WK4_YT_VIEWS_GROWTH WK5_YT_VIEWS_GROWTH WK6_YT_VIEWS_GROWTH WK7_YT_VIEWS_GROWTH WK8_YT_VIEWS_GROWTH WK9_YT_VIEWS_GROWTH WK10_YT_VIEWS_GROWTH WK11_YT_VIEWS_GROWTH WK12_YT_VIEWS_GROWTH WK13_YT_VIEWS_GROWTH WK14_YT_VIEWS_GROWTH WK15_YT_VIEWS_GROWTH WK1_SPOTIFY_PLAYS_GROWTH WK2_SPOTIFY_PLAYS_GROWTH WK3_SPOTIFY_PLAYS_GROWTH WK4_SPOTIFY_PLAYS_GROWTH WK5_SPOTIFY_PLAYS_GROWTH WK6_SPOTIFY_PLAYS_GROWTH WK7_SPOTIFY_PLAYS_GROWTH WK8_SPOTIFY_PLAYS_GROWTH WK9_SPOTIFY_PLAYS_GROWTH WK10_SPOTIFY_PLAYS_GROWTH WK11_SPOTIFY_PLAYS_GROWTH WK12_SPOTIFY_PLAYS_GROWTH WK13_SPOTIFY_PLAYS_GROWTH WK14_SPOTIFY_PLAYS_GROWTH WK15_SPOTIFY_PLAYS_GROWTH WK1_TIKTOK_POSTS_GROWTH WK2_TIKTOK_POSTS_GROWTH WK3_TIKTOK_POSTS_GROWTH WK4_TIKTOK_POSTS_GROWTH WK5_TIKTOK_POSTS_GROWTH WK6_TIKTOK_POSTS_GROWTH WK7_TIKTOK_POSTS_GROWTH WK8_TIKTOK_POSTS_GROWTH WK9_TIKTOK_POSTS_GROWTH WK10_TIKTOK_POSTS_GROWTH WK11_TIKTOK_POSTS_GROWTH WK12_TIKTOK_POSTS_GROWTH WK13_TIKTOK_POSTS_GROWTH WK14_TIKTOK_POSTS_GROWTH WK15_TIKTOK_POSTS_GROWTH Alternative Country Dance Electronic Hip-Hop/Rap House J-Pop Jazz Latin Latin Pop Other Pop R&B/Soul Rock
TRACK_NAME
Te Prefiero Lejos -0.801913 -0.066995 -0.078786 -0.028792 -0.588250 -0.096746 -0.038017 -0.01681 -0.017012 -0.014578 -0.512871 -0.082558 -0.082867 -0.010637 -0.019187 -0.040787 0.322898 0.047209 -0.061612 -0.024661 -0.022676 -0.115446 0.380679 -0.099082 -0.086349 0.0 -0.045817 -0.063735 -0.066255 -0.064269 -0.071527 -0.036888 -0.038715 -0.046073 -0.039754 -0.045333 -0.048384 0.0 -0.047626 -0.063571 -0.061336 -0.063292 -0.055601 -0.031456 -0.041776 -0.052006 -0.042994 -0.038376 -0.043929 0.0 -0.028637 -0.028579 -0.028214 -0.028046 -0.153532 -0.151827 -0.103864 -0.019720 -0.127255 -0.022176 -0.101869 0.0 -0.239679 -0.311199 -0.238074 -0.284444 -0.271678 -0.235229 -0.242684 -0.26056 -0.003946 -0.254593 0.708040 0.0 -0.054837 -0.088185 -0.084232 -0.090177 -0.097749 -0.082555 -0.108085 -0.074872 -0.078152 -0.093411 -0.084281 0.0 -0.032617 -0.030574 -0.048919 -0.112104 -0.10608 -0.053721 -0.067381 -0.069875 -0.090428 -0.08831 -0.081067 0.002849 0.021138 0.023017 0.028546 0.036744 0.023692 0.023985 0.028802 0.037078 0.045087 0.042905 0.045856 0.035091 0.042129 0.036897 0.007607 0.003512 0.005857 0.00048 0.008648 -0.002804 0.005426 -0.001721 0.000358 -0.000742 0.008724 0.006673 0.004161 0.005506 0.009518 0.005755 0.009826 0.009818 0.009259 0.01862 0.015899 0.016524 0.022105 0.02297 0.021076 0.021456 0.014533 0.017734 0.023244 0.02306 -0.219166 -0.173361 -0.176035 -0.218807 -0.337095 -0.151033 -0.174733 -0.160541 -0.229381 -0.150181 1.810576 -0.428909 -0.214563 -0.348573
Pour Out A Little Liquor -0.713816 -0.066995 -0.078786 -0.028792 2.208278 0.029257 -0.036984 -0.01681 -0.017012 -0.014578 0.051900 -0.006332 -0.082867 -0.010637 -0.019187 0.078347 -1.886162 0.047209 -0.061612 -0.024661 -0.022676 0.188656 3.552841 -0.099082 -0.086349 0.0 -0.045817 -0.063735 -0.066255 -0.064269 -0.071527 -0.036888 -0.038715 -0.046073 -0.039754 -0.045333 -0.048384 0.0 -0.047626 -0.063571 -0.061336 -0.063292 -0.055601 -0.031456 -0.041776 -0.052006 -0.042994 -0.038376 -0.043929 0.0 -0.028637 -0.028579 -0.028214 -0.028046 0.314375 0.287159 0.185762 0.020282 0.199781 0.019807 0.086437 0.0 -0.239679 -0.214722 -0.238074 -0.165289 -0.271678 -0.235229 -0.242684 -0.26056 -0.173262 5.289707 -0.093385 0.0 0.126956 -0.007015 0.124935 -0.090177 -0.097749 0.154872 0.035391 -0.074872 -0.078152 -0.093411 -0.084281 0.0 -0.032617 -0.030574 -0.048919 -0.112104 -0.10608 -0.053721 -0.067381 -0.069875 -0.090428 -0.08831 -0.081067 0.002849 0.021138 0.023017 0.028546 0.036744 0.023692 0.023985 0.028802 0.037078 0.045087 0.042905 0.045856 0.035091 0.042129 0.036897 0.007607 0.003512 0.005857 0.00048 0.008648 -0.002804 0.005426 -0.001721 0.000358 -0.000742 0.008724 0.006673 0.004161 0.005506 0.009518 0.005755 0.009826 0.009818 0.009259 0.01862 0.015899 -0.017350 0.025456 0.02297 0.022307 0.023849 0.014533 0.017734 0.023244 0.02306 -0.219166 -0.173361 -0.176035 -0.218807 2.966527 -0.151033 -0.174733 -0.160541 -0.229381 -0.150181 -0.552310 -0.428909 -0.214563 -0.348573

DBscan Modeling - YouTube¶

In [15]:
### Slice out WoW Columns 

qoq_youtube = df_sample_scaled[["Q1_YOUTUBE_VIEWS_GROWTH", "Q2_YOUTUBE_VIEWS_GROWTH", "Q3_YOUTUBE_VIEWS_GROWTH",
                       "Q4_YOUTUBE_VIEWS_GROWTH"]]
Grid search for optimum number of clusters¶
In [16]:
from sklearn.cluster import KMeans 
from sklearn.metrics import silhouette_samples, silhouette_score 
import time 

n_clusters = [3,4,5,6]

for i in n_clusters:
    start = time.time()
    clusterer = KMeans(n_clusters = i, random_state = 10)
    cluster_labels = clusterer.fit_predict(qoq_youtube)
    silhouette_avg = silhouette_score(qoq_youtube, cluster_labels)
    end = time.time()
    timetaken = end - start
    print("For n_clusters =", i, "The average silhouette score is:", silhouette_avg)
    print("This iteration took", timetaken)
    sample_silhouette_values = silhouette_samples(qoq_youtube, cluster_labels)
For n_clusters = 3 The average silhouette score is: 0.9945894587732068
This iteration took 21.140565156936646
For n_clusters = 4 The average silhouette score is: 0.9927833907607675
This iteration took 21.17417597770691
For n_clusters = 5 The average silhouette score is: 0.9816392760220527
This iteration took 21.210541009902954
For n_clusters = 6 The average silhouette score is: 0.9821774995554735
This iteration took 21.623109102249146
Determine optimum EPS¶
In [17]:
### First, determine EPS range 

from sklearn.neighbors import NearestNeighbors
from matplotlib import pyplot as plt


neighbors = NearestNeighbors(n_neighbors=8)
neighbors_fit = neighbors.fit(qoq_youtube)
distances, indices = neighbors_fit.kneighbors(qoq_youtube)

distances = np.sort(distances, axis=0)
distances = distances[:,1]
plt.axis([0, 51000, 0, 0.02])
plt.plot(distances)
Out[17]:
[<matplotlib.lines.Line2D at 0x7f915830a8b0>]
In [18]:
### Determine Optimum EPS 
from sklearn.cluster import DBSCAN    
    
eps_range = [0.001, 0.002, 0.003, 0.004, 0.005]

for i in eps_range:
    start = time.time()
    print("eps value is" + str(i))
    db = DBSCAN(eps = i, min_samples=8).fit(qoq_youtube)
    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
    core_samples_mask[db.core_sample_indices_] = True 
    labels = db.labels_
    silhouette_avg = silhouette_score(qoq_youtube, labels)
    print(set(labels))
    silhouette_avg = silhouette_score(qoq_youtube, labels)
    end = time.time()
    timetaken = end - start
    print("For eps value ="+ str(i), labels, "the average silhouette score is:", silhouette_avg)
    print("This iteration took", timetaken, "seconds")
    
eps value is0.001
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -1}
For eps value =0.001 [ 0  0  0 ...  0  0 -1] the average silhouette score is: 0.7571481334695498
This iteration took 68.994784116745 seconds
eps value is0.002
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, -1}
For eps value =0.002 [ 0  0  0 ...  0  0 -1] the average silhouette score is: 0.7402907798941708
This iteration took 70.48721098899841 seconds
eps value is0.003
{0, 1, 2, 3, 4, 5, 6, -1}
For eps value =0.003 [ 0  0  0 ...  0  0 -1] the average silhouette score is: 0.7841014590627013
This iteration took 71.33101677894592 seconds
eps value is0.004
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, -1}
For eps value =0.004 [ 0  0  0 ...  0  0 -1] the average silhouette score is: 0.7649543460662273
This iteration took 72.07980418205261 seconds
eps value is0.005
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -1}
For eps value =0.005 [ 0  0  0 ...  0  0 -1] the average silhouette score is: 0.7708989318835603
This iteration took 72.89665389060974 seconds
Determine optimum min_samples¶
In [19]:
min_samples = [7,8,9,10,11,12,13]

for i in min_samples: 
    start = time.time()
    db = DBSCAN(eps = 0.003, min_samples=i).fit(qoq_youtube)
    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
    core_samples_mask[db.core_sample_indices_] = True 
    labels = db.labels_
    silhouette_avg = silhouette_score(qoq_youtube, labels)
    end = time.time()
    timetaken = end - start
    
    print("For min_sample value =" +str(i), "clusters produced ="+ str(len(set(labels))))
    print("This iteration took", timetaken, "seconds")
For min_sample value =7 clusters produced =10
This iteration took 50.234596967697144 seconds
For min_sample value =8 clusters produced =8
This iteration took 50.025941133499146 seconds
For min_sample value =9 clusters produced =9
This iteration took 50.171427965164185 seconds
For min_sample value =10 clusters produced =7
This iteration took 50.31635308265686 seconds
For min_sample value =11 clusters produced =6
This iteration took 50.90813708305359 seconds
For min_sample value =12 clusters produced =6
This iteration took 49.9834508895874 seconds
For min_sample value =13 clusters produced =5
This iteration took 50.46366095542908 seconds
Build model with optimum parameters¶
In [20]:
clustering = DBSCAN(eps=0.003, min_samples=12).fit(qoq_youtube)
components = clustering.components_
labels =clustering.labels_
core_sample_indices = clustering.core_sample_indices_                      

qoq_youtube.insert(0, 'cluster',clustering.labels_)
### count values within clusters 
qoq_youtube.cluster.value_counts()
Out[20]:
 0    43532
-1     5262
 1       39
 3       17
 4       12
 2       12
Name: cluster, dtype: int64

Over 43k songs in cluster 0. Those songs show 0 growth or low growth. Remove an recluster the rest of the songs and evaluate result.

Remove the 0 songs¶
In [21]:
### slice out the data we want 
df_no_zero_youtube = qoq_youtube.query('cluster == -1' or 'cluster == 1' or 'cluster == 2' or 'cluster == 3' or 'cluster == 4')
### drop cluster column
df_no_zero_youtube = df_no_zero_youtube.drop(['cluster'], axis = 1)
Optimize paramaters again for new model¶
In [22]:
### Determine optimum Epsilon 

from sklearn.neighbors import NearestNeighbors
from matplotlib import pyplot as plt


neighbors = NearestNeighbors(n_neighbors=8)
neighbors_fit = neighbors.fit(df_no_zero_youtube)
distances, indices = neighbors_fit.kneighbors(df_no_zero_youtube)

distances = np.sort(distances, axis=0)
distances = distances[:,1]
plt.axis([0, 5300, 0, 1])
plt.plot(distances)
Out[22]:
[<matplotlib.lines.Line2D at 0x7f8a980ce5e0>]
Determine optimum clusters¶
In [23]:
n_clusters = [3,4,5,6]

for i in n_clusters:
    clusterer = KMeans(n_clusters = i, random_state = 10)
    cluster_labels = clusterer.fit_predict(df_no_zero_youtube)
    silhouette_avg = silhouette_score(df_no_zero_youtube, cluster_labels)
    
    print("For n_clusters =", i, "The average silhouette score is:", silhouette_avg)
    
    sample_silhouette_values = silhouette_samples(df_no_zero_youtube, cluster_labels)
For n_clusters = 3 The average silhouette score is: 0.9379727354949758
For n_clusters = 4 The average silhouette score is: 0.9408528351853975
For n_clusters = 5 The average silhouette score is: 0.8476584343370819
For n_clusters = 6 The average silhouette score is: 0.8542504814108389
Determine optimum eps¶
In [26]:
eps_range = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]

for i in eps_range:
    start = time.time()
    print("eps value is" + str(i))
    db = DBSCAN(eps = i, min_samples=8).fit(df_no_zero_youtube)
    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
    core_samples_mask[db.core_sample_indices_] = True 
    labels = db.labels_
    silhouette_avg = silhouette_score(df_no_zero_youtube, labels)
    print(set(labels))
    silhouette_avg = silhouette_score(df_no_zero_youtube, labels)
    end = time.time()
    timetaken = start - end
    print("For eps value ="+ str(i), labels, "the average silhouette score is:", silhouette_avg)
    print("This iteration took", timetaken)
eps value is0.1
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, -1}
For eps value =0.1 [0 0 0 ... 0 0 0] the average silhouette score is: 0.04638665279312412
This iteration took -0.6213250160217285
eps value is0.2
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -1}
For eps value =0.2 [0 0 0 ... 0 0 0] the average silhouette score is: 0.2870292358000253
This iteration took -0.6575460433959961
eps value is0.3
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, -1}
For eps value =0.3 [0 0 0 ... 0 0 0] the average silhouette score is: 0.34195394981649324
This iteration took -0.6816840171813965
eps value is0.4
{0, 1, 2, 3, 4, 5, 6, 7, -1}
For eps value =0.4 [0 0 0 ... 0 0 0] the average silhouette score is: 0.5240871644895779
This iteration took -0.7124831676483154
eps value is0.5
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -1}
For eps value =0.5 [0 0 0 ... 0 0 0] the average silhouette score is: 0.468714986437441
This iteration took -0.7362239360809326
eps value is0.6
{0, 1, 2, 3, 4, -1}
For eps value =0.6 [0 0 0 ... 0 0 0] the average silhouette score is: 0.5605654989465374
This iteration took -0.7342960834503174
eps value is0.7
{0, 1, 2, 3, 4, -1}
For eps value =0.7 [0 0 0 ... 0 0 0] the average silhouette score is: 0.576257889484914
This iteration took -0.7478110790252686
In [27]:
min_samples = [7,8,9,10,11,12,13, 14, 15]

for i in min_samples: 
    db = DBSCAN(eps = 0.5, min_samples=i).fit(df_no_zero_youtube)
    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
    core_samples_mask[db.core_sample_indices_] = True 
    labels = db.labels_
    silhouette_avg = silhouette_score(df_no_zero_youtube, labels)
    
    print("For min_sample value =" +str(i), "clusters produced ="+ str(len(set(labels))))
For min_sample value =7 clusters produced =10
For min_sample value =8 clusters produced =11
For min_sample value =9 clusters produced =8
For min_sample value =10 clusters produced =8
For min_sample value =11 clusters produced =9
For min_sample value =12 clusters produced =7
For min_sample value =13 clusters produced =5
For min_sample value =14 clusters produced =5
For min_sample value =15 clusters produced =4
Build model with optimum parameters¶
In [28]:
clustering = DBSCAN(eps=0.6, min_samples=15).fit(df_no_zero_youtube)
components = clustering.components_
labels =clustering.labels_
core_sample_indices = clustering.core_sample_indices_                      

df_no_zero_youtube.insert(0, 'cluster',clustering.labels_)
### count values within clusters 
df_no_zero_youtube.cluster.value_counts()
Out[28]:
 0    4615
-1     567
 1      41
 3      21
 2      18
Name: cluster, dtype: int64
In [29]:
clusters = df_no_zero_youtube \
    .groupby('cluster') \
    .agg('mean')

import matplotlib.pyplot as plt
from matplotlib import colors

def background_gradient(s, m, M, cmap='PuBu', low=0, high=0):
    rng = M - m
    norm = colors.Normalize(m - (rng * low),
                            M + (rng * high))
    normed = norm(s.values)
    c = [colors.rgb2hex(x) for x in plt.cm.get_cmap(cmap)(normed)]
    return ['background-color: %s' % color for color in c]

clusters.style.apply(background_gradient,
               cmap='Wistia',
               m=clusters.min().min(),
               M=clusters.max().max())
Out[29]:
  Q1_YOUTUBE_VIEWS_GROWTH Q2_YOUTUBE_VIEWS_GROWTH Q3_YOUTUBE_VIEWS_GROWTH Q4_YOUTUBE_VIEWS_GROWTH
cluster        
-1 3.325468 0.167801 0.452928 1.564610
0 0.216701 0.080405 0.053391 0.058367
1 -0.021774 6.295263 0.146793 0.111615
2 0.287102 0.127791 -2.960023 -0.034340
3 0.482192 0.102507 0.136515 -4.207431
In [30]:
import plotly.express as px

fig = px.scatter_3d(df_no_zero_youtube, x="Q1_YOUTUBE_VIEWS_GROWTH", y="Q2_YOUTUBE_VIEWS_GROWTH", z= 'Q3_YOUTUBE_VIEWS_GROWTH',
              color='cluster')
fig.show()
fig.update_layout(margin=dict(l=0, r=0, b=0, t=0))
In [31]:
### Investigate cluster -1 

df_negative_1 = df_no_zero_youtube.query('cluster == -1')
list_negative_1 = df_negative_1.index.tolist()

negative_1 = df.query("TRACK_NAME in @list_negative_1")
negative_1
Out[31]:
CM_TRACK TRACK_NAME CM_ALBUM ALBUM_NAME RELEASE_DATE ARTIST_NAME ARTIST_COUNTRY_CODE2 COUNTRY_NAME GENRE_ID ARTIST_GENRE DUPE Q1_YOUTUBE_VIEWS_GROWTH Q1_YOUTUBE_LIKES_GROWTH Q1_SPOTIFY_PLAYS_GROWTH Q1_SPOTIFY_POPULARITY_GROWTH Q1_TIKTOK_POSTS_GROWTH Q1_AIRPLAY_STREAMS_GROWTH Q2_YOUTUBE_VIEWS_GROWTH Q2_YOUTUBE_LIKES_GROWTH Q2_SPOTIFY_PLAYS_GROWTH Q2_SPOTIFY_POPULARITY_GROWTH Q2_TIKTOK_POSTS_GROWTH Q2_AIRPLAY_STREAMS_GROWTH Q3_YOUTUBE_VIEWS_GROWTH Q3_YOUTUBE_LIKES_GROWTH Q3_SPOTIFY_PLAYS_GROWTH Q3_SPOTIFY_POPULARITY_GROWTH Q3_TIKTOK_POSTS_GROWTH Q3_AIRPLAY_STREAMS_GROWTH Q4_YOUTUBE_VIEWS_GROWTH Q4_YOUTUBE_LIKES_GROWTH Q4_SPOTIFY_PLAYS_GROWTH Q4_SPOTIFY_POPULARITY_GROWTH Q4_TIKTOK_POSTS_GROWTH Q4_AIRPLAY_STREAMS_GROWTH GENRE M1_YOUTUBE_VIEWS_GROWTH M2_YOUTUBE_VIEWS_GROWTH M3_YOUTUBE_VIEWS_GROWTH M4_YOUTUBE_VIEWS_GROWTH M5_YOUTUBE_VIEWS_GROWTH M6_YOUTUBE_VIEWS_GROWTH M7_YOUTUBE_VIEWS_GROWTH M8_YOUTUBE_VIEWS_GROWTH M9_YOUTUBE_VIEWS_GROWTH M10_YOUTUBE_VIEWS_GROWTH M11_YOUTUBE_VIEWS_GROWTH M12_YOUTUBE_VIEWS_GROWTH M1_YOUTUBE_LIKES_GROWTH M2_YOUTUBE_LIKES_GROWTH M3_YOUTUBE_LIKES_GROWTH M4_YOUTUBE_LIKES_GROWTH M5_YOUTUBE_LIKES_GROWTH M6_YOUTUBE_LIKES_GROWTH M7_YOUTUBE_LIKES_GROWTH M8_YOUTUBE_LIKES_GROWTH M9_YOUTUBE_LIKES_GROWTH M10_YOUTUBE_LIKES_GROWTH M11_YOUTUBE_LIKES_GROWTH M12_YOUTUBE_LIKES_GROWTH M1_SPOTIFY_PLAYS_GROWTH M2_SPOTIFY_PLAYS_GROWTH M3_SPOTIFY_PLAYS_GROWTH M4_SPOTIFY_PLAYS_GROWTH M5_SPOTIFY_PLAYS_GROWTH M6_SPOTIFY_PLAYS_GROWTH M7_SPOTIFY_PLAYS_GROWTH M8_SPOTIFY_PLAYS_GROWTH M9_SPOTIFY_PLAYS_GROWTH M10_SPOTIFY_PLAYS_GROWTH M11_SPOTIFY_PLAYS_GROWTH M12_SPOTIFY_PLAYS_GROWTH M1_SPOTIFY_POPULARITY_GROWTH M2_SPOTIFY_POPULARITY_GROWTH M3_SPOTIFY_POPULARITY_GROWTH M4_SPOTIFY_POPULARITY_GROWTH M5_SPOTIFY_POPULARITY_GROWTH M6_SPOTIFY_POPULARITY_GROWTH M7_SPOTIFY_POPULARITY_GROWTH M8_SPOTIFY_POPULARITY_GROWTH M9_SPOTIFY_POPULARITY_GROWTH M10_SPOTIFY_POPULARITY_GROWTH M11_SPOTIFY_POPULARITY_GROWTH M12_SPOTIFY_POPULARITY_GROWTH M1_TIKTOK_POSTS_GROWTH M2_TIKTOK_POSTS_GROWTH M3_TIKTOK_POSTS_GROWTH M4_TIKTOK_POSTS_GROWTH M5_TIKTOK_POSTS_GROWTH M6_TIKTOK_POSTS_GROWTH M7_TIKTOK_POSTS_GROWTH M8_TIKTOK_POSTS_GROWTH M9_TIKTOK_POSTS_GROWTH M10_TIKTOK_POSTS_GROWTH M11_TIKTOK_POSTS_GROWTH M12_TIKTOK_POSTS_GROWTH M1_AIRPLAY_STREAMS_GROWTH M2_AIRPLAY_STREAMS_GROWTH M3_AIRPLAY_STREAMS_GROWTH M4_AIRPLAY_STREAMS_GROWTH M5_AIRPLAY_STREAMS_GROWTH M6_AIRPLAY_STREAMS_GROWTH M7_AIRPLAY_STREAMS_GROWTH M8_AIRPLAY_STREAMS_GROWTH M9_AIRPLAY_STREAMS_GROWTH M10_AIRPLAY_STREAMS_GROWTH M11_AIRPLAY_STREAMS_GROWTH M12_AIRPLAY_STREAMS_GROWTH WK1_YT_VIEWS_GROWTH WK2_YT_VIEWS_GROWTH WK3_YT_VIEWS_GROWTH WK4_YT_VIEWS_GROWTH WK5_YT_VIEWS_GROWTH WK6_YT_VIEWS_GROWTH WK7_YT_VIEWS_GROWTH WK8_YT_VIEWS_GROWTH WK9_YT_VIEWS_GROWTH WK10_YT_VIEWS_GROWTH WK11_YT_VIEWS_GROWTH WK12_YT_VIEWS_GROWTH WK13_YT_VIEWS_GROWTH WK14_YT_VIEWS_GROWTH WK15_YT_VIEWS_GROWTH WK1_SPOTIFY_PLAYS_GROWTH WK2_SPOTIFY_PLAYS_GROWTH WK3_SPOTIFY_PLAYS_GROWTH WK4_SPOTIFY_PLAYS_GROWTH WK5_SPOTIFY_PLAYS_GROWTH WK6_SPOTIFY_PLAYS_GROWTH WK7_SPOTIFY_PLAYS_GROWTH WK8_SPOTIFY_PLAYS_GROWTH WK9_SPOTIFY_PLAYS_GROWTH WK10_SPOTIFY_PLAYS_GROWTH WK11_SPOTIFY_PLAYS_GROWTH WK12_SPOTIFY_PLAYS_GROWTH WK13_SPOTIFY_PLAYS_GROWTH WK14_SPOTIFY_PLAYS_GROWTH WK15_SPOTIFY_PLAYS_GROWTH WK1_TIKTOK_POSTS_GROWTH WK2_TIKTOK_POSTS_GROWTH WK3_TIKTOK_POSTS_GROWTH WK4_TIKTOK_POSTS_GROWTH WK5_TIKTOK_POSTS_GROWTH WK6_TIKTOK_POSTS_GROWTH WK7_TIKTOK_POSTS_GROWTH WK8_TIKTOK_POSTS_GROWTH WK9_TIKTOK_POSTS_GROWTH WK10_TIKTOK_POSTS_GROWTH WK11_TIKTOK_POSTS_GROWTH WK12_TIKTOK_POSTS_GROWTH WK13_TIKTOK_POSTS_GROWTH WK14_TIKTOK_POSTS_GROWTH WK15_TIKTOK_POSTS_GROWTH
52 67519811 Lullaby 20930561 Lovesick 2022-04-29 Jay Isaiah CA Canada 462882.0 Pop 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Dance 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9924.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
111 19087846 Addicted 2460010 Addicted 2018-04-18 VanJess US United States 462889.0 R&B/Soul 1 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 -2.0 17.0 0.0 0.0 0.0 0.0 0.0 -13.0 0.0 0.0 0.0 0.0 0.0 10.0 0.0 R&B/Soul 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 14.0 3.0 0.0 0.0 11.0 0.0 10.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -19.0 0.0 -256.0 0.0 -24.0 0.0 -55.0
305 13381429 Hey Now 6310404 MAN WITH A "BEST" MISSION (Deluxe Edition) 2020-07-15 MAN WITH A MISSION JP Japan 462924.0 J-Pop 1 3212839.0 380.0 0.0 23.0 0.0 4.0 163390.0 620.0 0.0 4.0 0.0 1.0 155665.0 709.0 0.0 -17.0 0.0 2.0 163889.0 567.0 0.0 3.0 0.0 2.0 J-Pop 0.0 42325.0 52655.0 52105.0 54874.0 56411.0 56727.0 44611.0 54327.0 58400.0 56525.0 48964.0 0.0 122.0 160.0 180.0 223.0 217.0 237.0 230.0 242.0 224.0 209.0 134.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10.0 2.0 2.0 0.0 19.0 0.0 14.0 0.0 0.0 16.0 9.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 3.0 1.0 0.0 1.0 0.0 1.0 1.0 0.0 1.0 1.0 0.0 0.0 0.0 -309670367.0 0.0 0.0 0.0 0.0 1485514.0 0.0 0.0 1766148.0 0.0 -81464503.0 2887717.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
500 12265525 Selfie 3074223 Selfie 2015-07-06 Vald FR France 462883.0 Hip-Hop/Rap 1 3430.0 10.0 0.0 4.0 0.0 0.0 11615182.0 69715.0 0.0 48.0 0.0 0.0 51574.0 416.0 0.0 -51.0 0.0 0.0 -11666181.0 -70127.0 0.0 51.0 0.0 0.0 Hip-Hop/Rap 0.0 28944.0 0.0 11569596.0 0.0 11612730.0 16110.0 19008.0 16456.0 17967.0 19495.0 0.0 0.0 211.0 0.0 69322.0 0.0 69725.0 118.0 136.0 162.0 120.0 168.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 48.0 0.0 0.0 0.0 0.0 0.0 0.0 51.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 240293.0 11124691.0 0.0 0.0 0.0 0.0 317784.0 -20921733.0 0.0 0.0 11539083.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
509 21815691 Starlight 3365695 Starlight 2018-10-19 BABYMETAL JP Japan 462884.0 Rock 1 5179619.0 3264.0 0.0 22.0 47.0 4.0 156612.0 2625.0 0.0 24.0 20.0 0.0 153045.0 2502.0 0.0 -36.0 -62.0 5.0 129372.0 2046.0 0.0 16.0 73.0 4.0 Hard Rock 0.0 70830.0 75610.0 56308.0 56591.0 43713.0 51029.0 54787.0 47229.0 46026.0 42660.0 40686.0 0.0 1258.0 1409.0 909.0 978.0 738.0 901.0 868.0 733.0 751.0 654.0 641.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9.0 0.0 32.0 0.0 0.0 0.0 10.0 17.0 0.0 0.0 0.0 0.0 47.0 12.0 6.0 2.0 0.0 6.0 0.0 1.0 6.0 66.0 0.0 0.0 1.0 0.0 0.0 0.0 2.0 1.0 2.0 1.0 0.0 3.0 0.0 0.0 -308425708.0 0.0 -46136631.0 -5781012.0 0.0 -349708.0 0.0 0.0 1333673.0 0.0 -4050805.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -18748.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
101731 36087839 Cringe 8353312 Cringe / You're the Boss 2021-04-30 Sophiegrophy AU Australia 462883.0 Hip-Hop/Rap 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 18181.0 -7.0 0.0 0.0 0.0 0.0 12058.0 -1.0 1.0 0.0 Electronic 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9413.0 6944.0 5799.0 5438.0 3823.0 4538.0 3697.0 0.0 0.0 0.0 0.0 34.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
101993 14488464 Intoxicated - Radio Edit 3100017 House Every Weekend, Vol. 2 2016-03-11 Good Times Ahead US United States 462887.0 Electronic 1 164890036.0 886774.0 0.0 16.0 0.0 1966.0 -164549695.0 -895961.0 0.0 1.0 0.0 1475.0 -320677.0 -3967.0 9021688.0 -2.0 0.0 1548.0 171312941.0 955695.0 7668998.0 2.0 0.0 1375.0 Electronic 0.0 935575.0 1015869.0 1045060.0 752381.0 0.0 167600702.0 664096.0 0.0 169914064.0 666934.0 731943.0 0.0 6699.0 8561.0 7011.0 5700.0 0.0 918933.0 5116.0 0.0 939578.0 7623.0 8494.0 0.0 0.0 0.0 0.0 0.0 3350202.0 3223171.0 3040072.0 2758445.0 2750294.0 2595633.0 2323071.0 0.0 0.0 10.0 0.0 1.0 1.0 0.0 0.0 58.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 621.0 672.0 711.0 377.0 387.0 495.0 514.0 539.0 433.0 436.0 506.0 0.0 0.0 147613139.0 161835676.0 0.0 158263776.0 0.0 0.0 0.0 0.0 123682267.0 65697172.0 0.0 0.0 161511553.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
102072 12315279 Pose ton Gun 1798878 Pose Ton Gun 1999-09-13 SuprĂȘme NTM FR France 462883.0 Hip-Hop/Rap 1 20612933.0 67823.0 0.0 31.0 23.0 5.0 624635.0 3426.0 0.0 0.0 19.0 9.0 575967.0 3376.0 0.0 -22.0 0.0 3.0 -19606269.0 -68130.0 0.0 2.0 0.0 7.0 Hip-Hop/Rap 0.0 214443.0 214299.0 225924.0 223350.0 175361.0 176710.0 212980.0 186277.0 185571.0 171696.0 0.0 0.0 1173.0 1227.0 1192.0 1242.0 992.0 1002.0 1384.0 990.0 974.0 1204.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 115179.0 0.0 4.0 0.0 0.0 3.0 0.0 0.0 0.0 6.0 1.0 0.0 1.0 0.0 0.0 11.0 4.0 11.0 4.0 4.0 10.0 0.0 0.0 2.0 22.0 0.0 4.0 1.0 2.0 5.0 2.0 0.0 1.0 2.0 2.0 4.0 1.0 -674927955.0 -124377253.0 -531138.0 17032541.0 17441068.0 0.0 0.0 16608033.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -257.0 0.0 0.0 0.0 0.0 194.0 0.0
102128 59708880 All Night 11884894 HEY WHAT 2021-09-10 Low US United States 462888.0 Alternative 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 275325.0 0.0 0.0 39.0 Alternative 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 101052.0 55464.0 118809.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 22.0 5.0 12.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
102389 13469059 Lullaby 12138962 Mountains - Massive Rock Hits 2021-07-02 Nickelback CA Canada 462882.0 Pop 1 116202530.0 8699.0 0.0 34.0 0.0 0.0 1373131.0 7558.0 0.0 -1.0 0.0 0.0 1173909.0 7138.0 0.0 -35.0 0.0 0.0 1077496.0 6812.0 0.0 1.0 0.0 0.0 Metal 0.0 512034.0 574319.0 527586.0 483271.0 362274.0 340953.0 445594.0 387362.0 420203.0 334885.0 322408.0 0.0 2718.0 3095.0 2895.0 2617.0 2046.0 2007.0 2706.0 2425.0 2695.0 2014.0 2103.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 97897749.0 0.0 0.0 0.0 88628371.0 0.0 0.0 0.0 0.0 109205678.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

957 rows × 153 columns

In [32]:
negative_1.ARTIST_GENRE.value_counts()
Out[32]:
Pop            138
Rock           111
Hip-Hop/Rap     93
Latin           91
R&B/Soul        51
              ... 
Opera            1
Arabic           1
Dancehall        1
Reggaeton        1
Others           1
Name: ARTIST_GENRE, Length: 61, dtype: int64

Cluster -1 Country¶

In [71]:
df_youtube_cn1 = negative_1.COUNTRY_NAME.value_counts()
df_youtube_cn1 = pd.DataFrame(data=df_youtube_cn1)
df_youtube_cn1.columns = ['count']
df_youtube_cn1.plot.bar()
Out[71]:
<AxesSubplot:>

Cluster -1 Genre¶

In [70]:
df_youtube_cn1_genre = negative_1.ARTIST_GENRE.value_counts()
df_youtube_cn1_genre = pd.DataFrame(data=df_youtube_cn1_genre)
df_youtube_cn1_genre.columns = ['count']
df_youtube_cn1_genre.plot.bar()
Out[70]:
<AxesSubplot:>
In [33]:
### Investigate cluster 0

df_zero = df_no_zero_youtube.query('cluster == 0')
list_two = df_zero.index.tolist()

zero = df.query("TRACK_NAME in @list_two")
zero
Out[33]:
CM_TRACK TRACK_NAME CM_ALBUM ALBUM_NAME RELEASE_DATE ARTIST_NAME ARTIST_COUNTRY_CODE2 COUNTRY_NAME GENRE_ID ARTIST_GENRE DUPE Q1_YOUTUBE_VIEWS_GROWTH Q1_YOUTUBE_LIKES_GROWTH Q1_SPOTIFY_PLAYS_GROWTH Q1_SPOTIFY_POPULARITY_GROWTH Q1_TIKTOK_POSTS_GROWTH Q1_AIRPLAY_STREAMS_GROWTH Q2_YOUTUBE_VIEWS_GROWTH Q2_YOUTUBE_LIKES_GROWTH Q2_SPOTIFY_PLAYS_GROWTH Q2_SPOTIFY_POPULARITY_GROWTH Q2_TIKTOK_POSTS_GROWTH Q2_AIRPLAY_STREAMS_GROWTH Q3_YOUTUBE_VIEWS_GROWTH Q3_YOUTUBE_LIKES_GROWTH Q3_SPOTIFY_PLAYS_GROWTH Q3_SPOTIFY_POPULARITY_GROWTH Q3_TIKTOK_POSTS_GROWTH Q3_AIRPLAY_STREAMS_GROWTH Q4_YOUTUBE_VIEWS_GROWTH Q4_YOUTUBE_LIKES_GROWTH Q4_SPOTIFY_PLAYS_GROWTH Q4_SPOTIFY_POPULARITY_GROWTH Q4_TIKTOK_POSTS_GROWTH Q4_AIRPLAY_STREAMS_GROWTH GENRE M1_YOUTUBE_VIEWS_GROWTH M2_YOUTUBE_VIEWS_GROWTH M3_YOUTUBE_VIEWS_GROWTH M4_YOUTUBE_VIEWS_GROWTH M5_YOUTUBE_VIEWS_GROWTH M6_YOUTUBE_VIEWS_GROWTH M7_YOUTUBE_VIEWS_GROWTH M8_YOUTUBE_VIEWS_GROWTH M9_YOUTUBE_VIEWS_GROWTH M10_YOUTUBE_VIEWS_GROWTH M11_YOUTUBE_VIEWS_GROWTH M12_YOUTUBE_VIEWS_GROWTH M1_YOUTUBE_LIKES_GROWTH M2_YOUTUBE_LIKES_GROWTH M3_YOUTUBE_LIKES_GROWTH M4_YOUTUBE_LIKES_GROWTH M5_YOUTUBE_LIKES_GROWTH M6_YOUTUBE_LIKES_GROWTH M7_YOUTUBE_LIKES_GROWTH M8_YOUTUBE_LIKES_GROWTH M9_YOUTUBE_LIKES_GROWTH M10_YOUTUBE_LIKES_GROWTH M11_YOUTUBE_LIKES_GROWTH M12_YOUTUBE_LIKES_GROWTH M1_SPOTIFY_PLAYS_GROWTH M2_SPOTIFY_PLAYS_GROWTH M3_SPOTIFY_PLAYS_GROWTH M4_SPOTIFY_PLAYS_GROWTH M5_SPOTIFY_PLAYS_GROWTH M6_SPOTIFY_PLAYS_GROWTH M7_SPOTIFY_PLAYS_GROWTH M8_SPOTIFY_PLAYS_GROWTH M9_SPOTIFY_PLAYS_GROWTH M10_SPOTIFY_PLAYS_GROWTH M11_SPOTIFY_PLAYS_GROWTH M12_SPOTIFY_PLAYS_GROWTH M1_SPOTIFY_POPULARITY_GROWTH M2_SPOTIFY_POPULARITY_GROWTH M3_SPOTIFY_POPULARITY_GROWTH M4_SPOTIFY_POPULARITY_GROWTH M5_SPOTIFY_POPULARITY_GROWTH M6_SPOTIFY_POPULARITY_GROWTH M7_SPOTIFY_POPULARITY_GROWTH M8_SPOTIFY_POPULARITY_GROWTH M9_SPOTIFY_POPULARITY_GROWTH M10_SPOTIFY_POPULARITY_GROWTH M11_SPOTIFY_POPULARITY_GROWTH M12_SPOTIFY_POPULARITY_GROWTH M1_TIKTOK_POSTS_GROWTH M2_TIKTOK_POSTS_GROWTH M3_TIKTOK_POSTS_GROWTH M4_TIKTOK_POSTS_GROWTH M5_TIKTOK_POSTS_GROWTH M6_TIKTOK_POSTS_GROWTH M7_TIKTOK_POSTS_GROWTH M8_TIKTOK_POSTS_GROWTH M9_TIKTOK_POSTS_GROWTH M10_TIKTOK_POSTS_GROWTH M11_TIKTOK_POSTS_GROWTH M12_TIKTOK_POSTS_GROWTH M1_AIRPLAY_STREAMS_GROWTH M2_AIRPLAY_STREAMS_GROWTH M3_AIRPLAY_STREAMS_GROWTH M4_AIRPLAY_STREAMS_GROWTH M5_AIRPLAY_STREAMS_GROWTH M6_AIRPLAY_STREAMS_GROWTH M7_AIRPLAY_STREAMS_GROWTH M8_AIRPLAY_STREAMS_GROWTH M9_AIRPLAY_STREAMS_GROWTH M10_AIRPLAY_STREAMS_GROWTH M11_AIRPLAY_STREAMS_GROWTH M12_AIRPLAY_STREAMS_GROWTH WK1_YT_VIEWS_GROWTH WK2_YT_VIEWS_GROWTH WK3_YT_VIEWS_GROWTH WK4_YT_VIEWS_GROWTH WK5_YT_VIEWS_GROWTH WK6_YT_VIEWS_GROWTH WK7_YT_VIEWS_GROWTH WK8_YT_VIEWS_GROWTH WK9_YT_VIEWS_GROWTH WK10_YT_VIEWS_GROWTH WK11_YT_VIEWS_GROWTH WK12_YT_VIEWS_GROWTH WK13_YT_VIEWS_GROWTH WK14_YT_VIEWS_GROWTH WK15_YT_VIEWS_GROWTH WK1_SPOTIFY_PLAYS_GROWTH WK2_SPOTIFY_PLAYS_GROWTH WK3_SPOTIFY_PLAYS_GROWTH WK4_SPOTIFY_PLAYS_GROWTH WK5_SPOTIFY_PLAYS_GROWTH WK6_SPOTIFY_PLAYS_GROWTH WK7_SPOTIFY_PLAYS_GROWTH WK8_SPOTIFY_PLAYS_GROWTH WK9_SPOTIFY_PLAYS_GROWTH WK10_SPOTIFY_PLAYS_GROWTH WK11_SPOTIFY_PLAYS_GROWTH WK12_SPOTIFY_PLAYS_GROWTH WK13_SPOTIFY_PLAYS_GROWTH WK14_SPOTIFY_PLAYS_GROWTH WK15_SPOTIFY_PLAYS_GROWTH WK1_TIKTOK_POSTS_GROWTH WK2_TIKTOK_POSTS_GROWTH WK3_TIKTOK_POSTS_GROWTH WK4_TIKTOK_POSTS_GROWTH WK5_TIKTOK_POSTS_GROWTH WK6_TIKTOK_POSTS_GROWTH WK7_TIKTOK_POSTS_GROWTH WK8_TIKTOK_POSTS_GROWTH WK9_TIKTOK_POSTS_GROWTH WK10_TIKTOK_POSTS_GROWTH WK11_TIKTOK_POSTS_GROWTH WK12_TIKTOK_POSTS_GROWTH WK13_TIKTOK_POSTS_GROWTH WK14_TIKTOK_POSTS_GROWTH WK15_TIKTOK_POSTS_GROWTH
5 15905276 Everybody Knows 801953 Everybody Knows EP (International Version) 2007-01-01 Ryan Adams US United States 462891.0 Singer/Songwriter 1 2398687.0 93.0 0.0 32.0 0.0 20.0 11925.0 63.0 0.0 0.0 0.0 11.0 10860.0 54.0 0.0 -13.0 0.0 20.0 10756.0 62.0 0.0 2.0 0.0 23.0 Alternative 0.0 5786.0 5152.0 3484.0 4269.0 4172.0 3925.0 4275.0 2660.0 3453.0 3743.0 3560.0 0.0 40.0 45.0 19.0 22.0 22.0 24.0 17.0 13.0 20.0 20.0 22.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 14.0 0.0 13.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.0 4.0 5.0 5.0 1.0 9.0 5.0 6.0 10.0 8.0 5.0 0.0 0.0 -6208611.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1366261.0 0.0 0.0 2336211.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
27 15966354 Flex 147413 Flex (feat. Tory Lanez & Fabolous) - Single 2016-07-01 Joe Budden US United States 462883.0 Hip-Hop/Rap 1 1260510.0 88.0 0.0 2.0 0.0 0.0 7031.0 76.0 0.0 -1.0 0.0 0.0 6931.0 75.0 0.0 -3.0 0.0 0.0 5891.0 68.0 0.0 2.0 0.0 0.0 Hip-Hop/Rap 0.0 1848.0 2577.0 2534.0 2138.0 2359.0 2288.0 2168.0 2475.0 1527.0 2125.0 2239.0 0.0 38.0 25.0 23.0 27.0 26.0 22.0 25.0 28.0 21.0 22.0 25.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -9994086.0 0.0 -35213594.0 0.0 0.0 0.0 -7608523.0 -5207633.0 -5500156.0 949964.0 -344181195.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
52 67519811 Lullaby 20930561 Lovesick 2022-04-29 Jay Isaiah CA Canada 462882.0 Pop 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Dance 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9924.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
62 13123423 Red 204739 Bloodworks 2013-04-29 Phoria GB United Kingdom 462894.0 New Age 1 3244.0 437.0 0.0 37.0 0.0 2.0 51557.0 -201.0 0.0 -2.0 2.0 2.0 118495.0 1711.0 0.0 -16.0 0.0 2.0 -167598.0 -1494.0 0.0 -12.0 0.0 1.0 Electronic 0.0 0.0 0.0 37458.0 115480.0 0.0 106140.0 0.0 117265.0 0.0 37782.0 0.0 0.0 0.0 0.0 0.0 1838.0 0.0 1636.0 0.0 1708.0 0.0 0.0 450.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 26261.0 0.0 0.0 17.0 0.0 30.0 0.0 0.0 0.0 18.0 2.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 1.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 113207.0 -979203.0 0.0 0.0 0.0 -73192747.0 -129128.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -10.0 0.0 0.0 0.0 0.0 -1316.0 -335.0
73 28604194 Happy Days 6940264 Something to Say 2020-10-30 Cory Henry US United States 462933.0 Funk 1 0.0 0.0 0.0 13.0 2.0 12.0 0.0 0.0 0.0 -1.0 4.0 11.0 0.0 0.0 373025.0 0.0 5.0 6.0 0.0 0.0 415501.0 0.0 6.0 25.0 Funk 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 96013.0 102781.0 130579.0 139665.0 161427.0 120146.0 133928.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 2.0 3.0 0.0 0.0 6.0 1.0 0.0 0.0 3.0 4.0 4.0 4.0 3.0 1.0 2.0 3.0 5.0 10.0 10.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 -515.0 -855.0 0.0
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102532 17882234 There Are Many Ways To Say I Love You 5721641 Musique Love 2020-02-21 Sylvan Esso US United States 462884.0 Rock 1 5704.0 0.0 0.0 43.0 0.0 1.0 192772.0 2458.0 0.0 1.0 0.0 0.0 2575.0 39.0 0.0 -44.0 0.0 1.0 -195347.0 -2497.0 0.0 0.0 0.0 4.0 Alternative 0.0 1106.0 0.0 191049.0 0.0 192772.0 817.0 0.0 195347.0 0.0 196996.0 0.0 0.0 19.0 0.0 2433.0 0.0 2458.0 7.0 0.0 2497.0 0.0 2524.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 2.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 4.0 0.0 0.0 0.0 0.0 -11949793.0 0.0 0.0 0.0 -146655176.0 0.0 -158090438.0 0.0 -52014.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

7205 rows × 153 columns

Cluster 0 Country¶

In [69]:
df_youtube_c0 = zero.COUNTRY_NAME.value_counts()
df_youtube_c0 = pd.DataFrame(data=df_youtube_c0)
df_youtube_c0.columns = ['count']
df_youtube_c0.plot.bar()
Out[69]:
<AxesSubplot:>
/Users/angelikalin/opt/anaconda3/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning:

Glyph 146 (\x92) missing from current font.

Cluster 0 Genre¶

In [68]:
df_youtube_c0_genre = zero.ARTIST_GENRE.value_counts()
df_youtube_c0_genre = pd.DataFrame(data=df_youtube_c0_genre)
df_youtube_c0_genre.columns = ['count']
df_youtube_c0_genre.plot.bar()
Out[68]:
<AxesSubplot:>
In [37]:
### Investigate cluster 1

df_one = df_no_zero_youtube.query('cluster == 1')
list_one = df_one.index.tolist()

one = df.query("TRACK_NAME in @list_one")
one
Out[37]:
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62295 15265838 Mouth For War 9641021 Kickass Metal Anthems 2021-05-14 Pantera US United States 462884.0 Rock 1 409331.0 535.0 0.0 59.0 88.0 59.0 17044744.0 115283.0 0.0 0.0 13.0 54.0 580367.0 5962.0 0.0 -2.0 -117.0 0.0 528384.0 5430.0 0.0 2.0 119.0 0.0 Hard Rock 0.0 186388.0 0.0 23570.0 24027.0 16997147.0 0.0 17335233.0 196751.0 175435.0 170129.0 182820.0 0.0 1846.0 0.0 300.0 308.0 114675.0 0.0 117923.0 2111.0 1789.0 1737.0 1904.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 590071.0 0.0 0.0 59.0 0.0 38.0 0.0 0.0 0.0 37.0 2.0 0.0 0.0 0.0 73.0 13.0 3.0 3.0 7.0 0.0 11.0 8.0 28.0 54.0 37.0 0.0 19.0 20.0 17.0 12.0 25.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 16165461.0 0.0 -3708840.0 14287112.0 0.0 16538042.0 0.0 0.0 0.0 -246939154.0 16651870.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -3753.0 0.0 0.0 0.0 0.0 -376.0 -23922.0 0.0
62500 13495602 Hey 3912712 Hey! 1980-01-01 Julio Iglesias ES Spain 462951.0 Latin 1 1869814.0 651.0 0.0 6.0 0.0 60.0 13122894.0 47100.0 0.0 17.0 0.0 57.0 532382.0 3323.0 0.0 -2.0 0.0 20.0 493781.0 3314.0 0.0 3.0 0.0 14.0 Latin 0.0 0.0 45345.0 12756001.0 166504.0 200389.0 190424.0 0.0 13232545.0 166100.0 150448.0 177233.0 0.0 0.0 311.0 44874.0 997.0 1229.0 1107.0 0.0 47830.0 1130.0 1127.0 1057.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 16.0 1.0 0.0 0.0 0.0 2.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 19.0 15.0 21.0 19.0 17.0 13.0 3.0 4.0 7.0 4.0 3.0 0.0 -582982.0 0.0 0.0 12398741.0 0.0 -7463365.0 317925.0 0.0 0.0 0.0 -1826002.0 -17635428.0 0.0 1676097.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
66096 11486035 Wat is' denn los mit Dir 3098392 Wat is' denn los mit dir 2014-04-25 Majoe DE Germany 462883.0 Hip-Hop/Rap 1 1520629.0 78241.0 0.0 40.0 3.0 0.0 13811866.0 92493.0 0.0 -1.0 32.0 0.0 65847.0 896.0 0.0 -39.0 0.0 0.0 48426.0 634.0 0.0 0.0 0.0 0.0 Hip-Hop/Rap 0.0 25107.0 0.0 13761174.0 27173.0 23519.0 24311.0 0.0 13836684.0 17657.0 0.0 13871472.0 0.0 389.0 0.0 91791.0 364.0 338.0 339.0 0.0 92601.0 273.0 0.0 93002.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 1.0 10.0 21.0 9.0 8.0 0.0 0.0 1.0 6.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6739602.0 9861746.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
69715 14086688 Hate To Say I Told You So 3927853 Hate To Say I Told You So 2001-04-03 The Hives SE Sweden 462884.0 Rock 1 3123.0 0.0 0.0 11.0 121.0 579.0 14825705.0 120710.0 0.0 -6.0 -38.0 771.0 906623.0 8734.0 9256760.0 5.0 0.0 962.0 975214.0 9351.0 9009180.0 2.0 0.0 817.0 Rock 0.0 230217.0 0.0 14142983.0 359384.0 323338.0 344475.0 272743.0 289405.0 353036.0 302061.0 320117.0 0.0 1872.0 0.0 110500.0 6582.0 3628.0 3598.0 2439.0 2697.0 3233.0 2855.0 3263.0 0.0 0.0 0.0 0.0 0.0 2917892.0 3238701.0 3092815.0 2925244.0 3214277.0 2894682.0 2900221.0 0.0 0.0 2.0 0.0 0.0 0.0 5.0 0.0 0.0 2.0 0.0 1.0 0.0 0.0 58.0 0.0 52.0 13.0 17.0 10.0 0.0 0.0 0.0 0.0 0.0 190.0 197.0 209.0 262.0 300.0 417.0 254.0 291.0 277.0 292.0 248.0 -7117435.0 0.0 -3374427.0 0.0 13049754.0 0.0 0.0 13173184.0 -108058617.0 0.0 13583372.0 0.0 0.0 13713576.0 -4608684.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -21.0 0.0 0.0 0.0 0.0 -350.0 -188.0 0.0 0.0 -575.0 0.0 0.0 104.0
70310 11082938 Enquanto Houver Sol 7368100 Seleção Essencial - Grandes Sucessos - Titãs 2010-01-01 Titãs BR Brazil 462884.0 Rock 1 368920.0 52.0 0.0 13.0 199.0 329.0 12944400.0 59340.0 0.0 -12.0 21.0 103.0 183322.0 3635.0 2584117.0 10.0 -328.0 109.0 98696.0 1869.0 2525092.0 3.0 727.0 219.0 Rock 0.0 47256.0 0.0 12857202.0 0.0 12942931.0 0.0 13065660.0 59080.0 32677.0 32240.0 33779.0 0.0 730.0 0.0 57682.0 0.0 59314.0 0.0 61797.0 1112.0 556.0 541.0 772.0 0.0 0.0 0.0 0.0 0.0 789847.0 829489.0 866348.0 888280.0 806663.0 741026.0 977403.0 0.0 0.0 2.0 0.0 0.0 0.0 10.0 0.0 52.0 1.0 0.0 2.0 0.0 180.0 16.0 11.0 2.0 8.0 0.0 13.0 0.0 243.0 388.0 96.0 0.0 175.0 33.0 31.0 40.0 32.0 26.0 52.0 31.0 60.0 78.0 81.0 0.0 0.0 -100758577.0 0.0 0.0 12393902.0 12674078.0 0.0 13107535.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -434.0 0.0 -85702.0 204.0 0.0 0.0 0.0
70496 14788274 Solitaire / Unraveling 934721 M3 1999-03-09 Mushroomhead US United States 462898.0 Metal 1 1594572.0 1023.0 0.0 3.0 1.0 0.0 13572320.0 62108.0 0.0 0.0 18.0 0.0 99639.0 1185.0 330340.0 0.0 33.0 0.0 86440.0 1078.0 338594.0 0.0 53.0 0.0 Hard Rock 0.0 30699.0 0.0 13510059.0 33938.0 28323.0 31839.0 0.0 13426474.0 31936.0 26068.0 28436.0 0.0 385.0 0.0 61352.0 429.0 327.0 383.0 0.0 61098.0 387.0 319.0 372.0 0.0 0.0 0.0 0.0 0.0 121746.0 104798.0 118238.0 107304.0 116458.0 110345.0 111791.0 0.0 0.0 3.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 2.0 7.0 9.0 13.0 15.0 5.0 17.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1182241.0 0.0 0.0 13676777.0 0.0 0.0 -183667011.0 -17301417.0 -224379382.0 6522074.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -897.0 -8.0 -63.0 -819.0
73512 15550603 The One Who Really Loves You - Single Version 480890 60s Summer Party: The Best Summer Hits from th... 2013-05-05 Mary Wells US United States 462949.0 Motown 1 1812054.0 2412.0 0.0 0.0 485.0 11.0 15800689.0 69252.0 0.0 48.0 46.0 12.0 179029.0 1433.0 502954.0 -41.0 -729.0 12.0 157758.0 1195.0 492156.0 42.0 1290.0 12.0 Motown 0.0 68971.0 0.0 15685453.0 62589.0 52647.0 62489.0 65910.0 50630.0 60579.0 48655.0 48524.0 0.0 383.0 0.0 68485.0 454.0 313.0 479.0 554.0 400.0 444.0 399.0 352.0 0.0 0.0 0.0 0.0 0.0 161827.0 161440.0 172949.0 168565.0 168645.0 158800.0 164711.0 0.0 0.0 0.0 13.0 35.0 0.0 0.0 0.0 5.0 0.0 48.0 1.0 0.0 67.0 51.0 16.0 30.0 0.0 0.0 2.0 0.0 737.0 536.0 17.0 0.0 3.0 5.0 4.0 3.0 5.0 4.0 6.0 2.0 6.0 2.0 4.0 0.0 0.0 0.0 0.0 0.0 -22385751.0 0.0 0.0 8790317.0 14775876.0 0.0 0.0 0.0 -4439198.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 551.0 0.0 0.0 0.0 309.0 0.0 660.0 0.0 0.0 0.0 0.0
73647 13419840 No MĂĄs Llorar 997153 Hasta la RaĂ­z (EdiciĂłn Especial) 2015-11-13 Natalia Lafourcade MX Mexico 462951.0 Latin 1 127003.0 47.0 0.0 43.0 0.0 0.0 12787944.0 27824.0 0.0 -1.0 0.0 0.0 72953.0 572.0 0.0 0.0 0.0 0.0 96890.0 533.0 0.0 0.0 3.0 0.0 Latin 0.0 24555.0 0.0 12738131.0 0.0 12777399.0 0.0 12814535.0 25397.0 26036.0 36811.0 34043.0 0.0 174.0 0.0 27537.0 0.0 27746.0 0.0 28052.0 184.0 157.0 196.0 180.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1102842.0 9216977.0 0.0 -14058746.0 5226882.0 -47400902.0 11952547.0 12196232.0 0.0 0.0 -62656459.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
75213 15478178 O LeĂŁozinho 853189 Brazil Classics 1: Beleza Tropical 2000-03-27 Caetano Veloso BR Brazil 462959.0 Latin Rock 1 1161800.0 1068.0 0.0 10.0 2.0 0.0 13858875.0 135758.0 0.0 16.0 -3.0 0.0 291052.0 5643.0 0.0 -2.0 -120.0 0.0 269725.0 5004.0 0.0 2.0 119.0 0.0 Jazz 0.0 0.0 25773.0 13642232.0 108762.0 107881.0 99583.0 0.0 14054125.0 92764.0 82049.0 94912.0 0.0 0.0 422.0 131760.0 2048.0 1950.0 1940.0 0.0 139693.0 1764.0 1586.0 1654.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 26.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 122.0 0.0 117.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12465606.0 0.0 0.0 0.0 12881149.0 0.0 0.0 -170246907.0 0.0 0.0 0.0 11962276.0 -9482039.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1804.0 -3559.0 0.0 -17360.0 0.0 13.0 54.0
76719 31303716 Close To You 6715057 Close To You 2020-09-11 None None None NaN None 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 46630.0 0.0 0.0 0.0 None 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 30840.0 27779.0 20467.0 16770.0 18217.0 13392.0 15021.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
76800 13712471 Roam 600916 Theia 2017-06-30 Theia NZ New Zealand 462882.0 Pop 1 0.0 0.0 0.0 5.0 0.0 14.0 0.0 0.0 0.0 -4.0 0.0 1.0 0.0 0.0 56819.0 -7.0 0.0 0.0 0.0 0.0 54612.0 4.0 0.0 0.0 Pop 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20100.0 20343.0 18692.0 17784.0 19433.0 16632.0 18547.0 0.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
76868 15394449 Ins and Outs 3932971 Ins and Outs 2017-08-25 Sofia Carson US United States 462882.0 Pop 1 2555.0 22.0 0.0 42.0 0.0 0.0 15778681.0 321034.0 0.0 -40.0 0.0 0.0 153394.0 1896.0 0.0 -2.0 11.0 0.0 142573.0 1585.0 0.0 0.0 26.0 0.0 Pop 0.0 76873.0 0.0 15653226.0 69960.0 55495.0 56780.0 52290.0 44324.0 52276.0 0.0 16051781.0 0.0 856.0 0.0 319333.0 914.0 787.0 722.0 615.0 559.0 624.0 0.0 323674.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 42.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 14.0 0.0 4.0 6.0 1.0 22.0 3.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 14388218.0 0.0 14980526.0 0.0 0.0 0.0 15342181.0 0.0 -9507408.0 15580945.0 1206587.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
79011 11088595 Setevidas 57243 SETEVIDAS 2014-06-03 Pitty BR Brazil 462884.0 Rock 1 352124.0 3619.0 0.0 43.0 0.0 8.0 14255573.0 164901.0 0.0 0.0 261.0 8.0 163430.0 2750.0 0.0 0.0 -194.0 8.0 165026.0 2884.0 0.0 0.0 229.0 8.0 Rock 0.0 0.0 0.0 14143085.0 0.0 14251446.0 61614.0 46466.0 55350.0 60270.0 51554.0 53202.0 0.0 0.0 0.0 163088.0 0.0 164849.0 957.0 791.0 1002.0 1134.0 905.0 845.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 67.0 206.0 0.0 285.0 0.0 0.0 77.0 229.0 0.0 2.0 4.0 2.0 4.0 2.0 2.0 3.0 3.0 4.0 1.0 3.0 0.0 0.0 0.0 0.0 0.0 -27754371.0 -744529574.0 0.0 13872542.0 13660983.0 0.0 0.0 0.0 0.0 -124735674.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20.0 0.0 -3241.0
79866 57508396 Close To You 14056044 Close To You - Single 2021-08-18 None None None NaN None 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 None 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
80185 11096862 Sou O Cara Pra VocĂȘ 554864 Ousadia & Alegria 2012-07-10 Thiaguinho BR Brazil 462883.0 Hip-Hop/Rap 1 4535513.0 288.0 0.0 54.0 0.0 4.0 14204632.0 130362.0 0.0 -3.0 0.0 11.0 596081.0 8295.0 0.0 -51.0 -52.0 10.0 544149.0 8085.0 0.0 54.0 75.0 5.0 Hip-Hop/Rap 0.0 0.0 13474.0 9229.0 10151.0 14185252.0 0.0 14547638.0 215331.0 0.0 15100726.0 180853.0 0.0 0.0 98.0 80.0 95.0 130187.0 0.0 135282.0 3054.0 0.0 143621.0 2613.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 50.0 0.0 52.0 0.0 2.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 53.0 0.0 57.0 4.0 14.0 0.0 1.0 2.0 3.0 2.0 6.0 0.0 8.0 2.0 0.0 5.0 0.0 0.0 0.0 0.0 0.0 16512551.0 0.0 16759871.0 0.0 14203451.0 0.0 0.0 4275233.0 0.0 -212856516.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
80861 17848362 Dengo Nego 3099867 Dengo Nego 2017-12-08 Mumuzinho BR Brazil 462882.0 Pop 1 2207048.0 14989.0 0.0 48.0 0.0 13.0 15391111.0 87458.0 0.0 -2.0 0.0 7.0 222173.0 1640.0 0.0 -46.0 -23.0 14.0 181044.0 1188.0 0.0 5.0 59.0 8.0 Hip-Hop/Rap 0.0 0.0 25153.0 0.0 16653903.0 81338.0 0.0 19922.0 15493334.0 56906.0 65906.0 58232.0 0.0 0.0 258.0 0.0 97215.0 534.0 0.0 187.0 87843.0 346.0 469.0 373.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 48.0 0.0 1.0 0.0 0.0 0.0 0.0 3.0 1.0 1.0 0.0 0.0 0.0 0.0 6.0 10.0 0.0 0.0 2.0 4.0 3.0 52.0 0.0 6.0 2.0 3.0 3.0 1.0 6.0 4.0 4.0 3.0 1.0 4.0 0.0 0.0 15604134.0 -6639476.0 0.0 14516767.0 0.0 -768616.0 15381481.0 654452.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
82837 11112052 Um Beijo 699179 Sertanejo Pop Festival 2011 - Ao Vivo 2011-05-20 Luan Santana BR Brazil 462877.0 Sertanejo 1 6233261.0 16.0 0.0 38.0 0.0 32.0 15159540.0 196628.0 0.0 0.0 0.0 15.0 1731082.0 12315.0 0.0 -21.0 0.0 16.0 1549557.0 10690.0 0.0 25.0 0.0 14.0 Pop 0.0 0.0 905.0 14011955.0 626552.0 521033.0 526676.0 638627.0 565779.0 600160.0 532961.0 416436.0 0.0 0.0 9.0 188003.0 4679.0 3946.0 3984.0 4553.0 3778.0 4164.0 3443.0 3083.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 21.0 0.0 20.0 0.0 0.0 18.0 0.0 23.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10.0 11.0 3.0 5.0 7.0 8.0 3.0 5.0 3.0 7.0 4.0 0.0 -912845.0 0.0 -44663037.0 0.0 14878671.0 -14986812.0 -11848951.0 0.0 6122853.0 985684.0 0.0 -9341812.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
83082 24234291 Acid Rain 4559107 Acid Rap 2013-04-30 Chance the Rapper US United States 462883.0 Hip-Hop/Rap 1 365584.0 444.0 1561766.0 2.0 20.0 12.0 13724640.0 68072.0 0.0 -1.0 44.0 13.0 55360.0 491.0 0.0 -2.0 0.0 15.0 55881.0 504.0 1714717.0 3.0 0.0 13.0 Hip-Hop/Rap 0.0 24923.0 0.0 13682286.0 0.0 13699711.0 19844.0 16363.0 19153.0 20665.0 17319.0 17897.0 0.0 202.0 0.0 67720.0 0.0 67762.0 175.0 153.0 163.0 165.0 164.0 175.0 0.0 500022.0 544816.0 521518.0 531910.0 0.0 0.0 564234.0 354786.0 657571.0 529173.0 527973.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 1.0 0.0 0.0 16.0 14.0 22.0 8.0 18.0 7.0 0.0 0.0 0.0 0.0 0.0 5.0 3.0 4.0 5.0 4.0 5.0 5.0 5.0 3.0 5.0 5.0 0.0 0.0 0.0 0.0 -44146718.0 2072124.0 0.0 0.0 0.0 12598941.0 0.0 13859112.0 6892541.0 0.0 -274270734.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1161.0 207.0 -4599.0
89246 15467451 Closer/Wrap Me In Your Arms 4008185 As We Worship Live 2009-02-03 William McDowell US United States 462934.0 Gospel 1 376538.0 37.0 0.0 41.0 0.0 0.0 12115134.0 54615.0 0.0 -1.0 0.0 0.0 269193.0 1832.0 0.0 -40.0 0.0 1.0 267619.0 1883.0 0.0 0.0 0.0 0.0 Christian 0.0 0.0 1379.0 11969768.0 0.0 12112719.0 0.0 12261430.0 118146.0 76282.0 99450.0 91887.0 0.0 0.0 14.0 53509.0 0.0 54586.0 0.0 55624.0 771.0 603.0 692.0 588.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 40648.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6923551.0 -70627765.0 -4866416.0 0.0 0.0 0.0 10367742.0 0.0 0.0 12151509.0 0.0 4095839.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
89354 15812336 Bark at the Moon 1653993 Halloween Classics: Hellbent For Halloween 2007-10-09 Ozzy Osbourne US United States 462898.0 Metal 1 2147.0 0.0 0.0 66.0 0.0 611.0 14537662.0 134145.0 0.0 0.0 0.0 701.0 1475489.0 12156.0 4322531.0 -2.0 0.0 563.0 1657454.0 13688.0 4646285.0 2.0 0.0 679.0 Hard Rock 0.0 0.0 0.0 13567534.0 493868.0 476260.0 0.0 2082.0 15691964.0 0.0 17102659.0 488388.0 0.0 0.0 0.0 125504.0 4583.0 4058.0 0.0 29.0 144030.0 0.0 154965.0 4015.0 0.0 0.0 0.0 0.0 0.0 1379989.0 1393373.0 1517772.0 1411386.0 1734167.0 1605952.0 1306166.0 0.0 0.0 53.0 0.0 52.0 1.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 194.0 228.0 229.0 243.0 229.0 192.0 186.0 185.0 294.0 161.0 224.0 0.0 0.0 0.0 -41805025.0 11118424.0 0.0 0.0 -1632569.0 7577255.0 0.0 0.0 0.0 0.0 0.0 -1329070.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
93852 22475459 Close To You 3842093 Close To You 2019-02-22 Alice Ivy AU Australia 462882.0 Pop 1 0.0 0.0 0.0 2.0 0.0 7.0 0.0 0.0 0.0 -3.0 0.0 8.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 10.0 Dance 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 5.0 1.0 3.0 4.0 1.0 1.0 1.0 6.0 1.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
97981 29441531 Game Over 6044423 Game Over 2020-05-15 Jhay Cortez PR Puerto Rico 462958.0 Latin Hip-Hop/Rap 1 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 12.0 0.0 0.0 0.0 0.0 0.0 26.0 0.0 Latin 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 12.0 4.0 4.0 5.0 3.0 12.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
98205 68290632 Me Gusta 17519504 Me Gusta 2021-12-10 ElArturo MX Mexico 462958.0 Latin Hip-Hop/Rap 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Latin 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 NaN 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 NaN 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 NaN 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 NaN 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 NaN 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 NaN 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
100017 11653362 Schmetterling 8144601 Electro Ghetto (Re-Release) 2004-01-01 Bushido DE Germany 462883.0 Hip-Hop/Rap 1 2274154.0 16773.0 0.0 46.0 33.0 1.0 15367916.0 123404.0 0.0 3.0 231.0 1.0 399611.0 2968.0 0.0 -49.0 142.0 0.0 348888.0 2529.0 0.0 54.0 234.0 0.0 Hip-Hop/Rap 0.0 151484.0 0.0 15140006.0 133172.0 94738.0 135428.0 150459.0 113724.0 123231.0 105327.0 120330.0 0.0 888.0 0.0 121632.0 993.0 779.0 1082.0 1015.0 871.0 950.0 784.0 795.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 1.0 0.0 0.0 51.0 3.0 0.0 62.0 0.0 53.0 99.0 79.0 35.0 69.0 38.0 81.0 56.0 97.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3980417.0 -5182517.0 0.0 0.0 0.0 0.0 0.0 12272715.0 11530361.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 920.0 0.0 0.0 0.0 0.0 0.0 0.0 14.0 0.0 -242.0 0.0

Cluster 1 Country¶

In [48]:
df_youtube_c1 = one.COUNTRY_NAME.value_counts()
df_youtube_c1 = pd.DataFrame(data=df_youtube_c1)
df_youtube_c1.columns = ['count']
df_youtube_c1.plot.bar()
Out[48]:
<AxesSubplot:>

Cluster 1 Genre¶

In [49]:
df_youtube_c1_genre = one.ARTIST_GENRE.value_counts()
df_youtube_c1_genre = pd.DataFrame(data=df_youtube_c1_genre)
df_youtube_c1_genre.columns = ['count']
df_youtube_c1_genre.plot.bar()
Out[49]:
<AxesSubplot:>
In [39]:
### Investigate cluster 2

df_2 = df_no_zero_youtube.query('cluster == 2')
list_2 = df_2.index.tolist()

two = df.query("TRACK_NAME in @list_2")
two
Out[39]:
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58288 15829591 You and I 1889104 I Am 1979-06-01 Earth, Wind & Fire US United States 462889.0 R&B/Soul 1 0.0 0.0 0.0 40.0 22.0 1.0 0.0 0.0 0.0 -15.0 7.0 3.0 0.0 0.0 0.0 -1.0 -118.0 7.0 0.0 0.0 0.0 1.0 116.0 2.0 R&B/Soul 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 25.0 0.0 13.0 1.0 2.0 4.0 1.0 3.0 0.0 0.0 0.0 114.0 2.0 0.0 0.0 1.0 2.0 1.0 0.0 5.0 0.0 2.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -357.0 0.0 0.0 0.0 0.0 0.0 103.0 -25688.0 0.0 0.0 0.0 0.0 -286.0 -1278.0 109.0
60643 18072028 You and I 5145535 Best of 2018 2018-12-07 ALPHA 9 RU Russian Federation 462927.0 Trance 1 0.0 0.0 0.0 6.0 0.0 0.0 0.0 0.0 0.0 27.0 0.0 0.0 0.0 0.0 0.0 -12.0 2.0 0.0 0.0 0.0 0.0 -21.0 1.0 0.0 Electronic 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 27.0 1.0 0.0 0.0 23.0 0.0 0.0 23.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
62445 61549041 You and I 15073143 This Time I See It 2021-09-24 The Franklin Electric CA Canada 462884.0 Rock 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Alternative 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 32165.0 26392.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
68417 11102884 Mesmo Que Seja Eu 10986506 Erasmo 80 2021-06-03 Erasmo Carlos BR Brazil 462882.0 Pop 1 6597585.0 40600.0 0.0 14.0 9.0 8.0 120532.0 1300.0 0.0 1.0 2.0 4.0 -5488327.0 -36436.0 0.0 -20.0 -2.0 6.0 779476.0 17760.0 0.0 2.0 46.0 5.0 Pop 0.0 5354525.0 51720.0 0.0 5468357.0 42480.0 46819.0 50196.0 0.0 5673981.0 0.0 133398.0 0.0 35309.0 469.0 0.0 36233.0 533.0 406.0 441.0 0.0 38212.0 0.0 1461.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 117505.0 0.0 0.0 2.0 0.0 1.0 2.0 0.0 0.0 1.0 1.0 0.0 2.0 0.0 0.0 6.0 0.0 0.0 2.0 0.0 14.0 0.0 26.0 7.0 13.0 0.0 3.0 0.0 1.0 3.0 0.0 4.0 1.0 1.0 2.0 0.0 3.0 0.0 0.0 0.0 -1531917.0 6191846.0 0.0 0.0 0.0 0.0 0.0 6234417.0 0.0 -4070324.0 6111699.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 19.0 -1740.0
68497 13377852 損才ぼăȘいæ€Ș物 1026864 損才ぼăȘいæ€Ș物 2012-11-27 EGOIST JP Japan 462885.0 Dance 1 11722032.0 74834.0 0.0 10.0 0.0 2.0 228082.0 2667.0 0.0 3.0 0.0 7.0 -7068277.0 -75677.0 1161895.0 -2.0 78.0 5.0 61633.0 586.0 1278571.0 5.0 32.0 7.0 Dance 0.0 76691.0 96933.0 77255.0 0.0 7160910.0 0.0 22551.0 27607.0 25656.0 15919.0 20058.0 0.0 890.0 1011.0 814.0 0.0 76520.0 0.0 192.0 269.0 260.0 141.0 185.0 0.0 0.0 0.0 0.0 0.0 386501.0 383646.0 396948.0 381301.0 441537.0 410849.0 426185.0 0.0 0.0 5.0 1.0 1.0 1.0 0.0 0.0 0.0 3.0 0.0 2.0 0.0 0.0 0.0 0.0 2.0 0.0 1.0 0.0 0.0 7.0 10.0 15.0 0.0 1.0 0.0 0.0 1.0 6.0 4.0 0.0 1.0 2.0 4.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -6264526.0 0.0 10212993.0 6264029.0 -49230364.0 0.0 0.0 0.0 11456732.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
69016 18581755 Colors of the Heart 2715599 ALL TIME BEST -FAN BEST- 2018-07-18 UVERworld JP Japan 462883.0 Hip-Hop/Rap 1 6643673.0 86349.0 0.0 7.0 20.0 20.0 235779.0 2744.0 0.0 -2.0 -1.0 17.0 -6594068.0 -89053.0 0.0 -26.0 -175.0 10.0 1069.0 19.0 0.0 3.0 10.0 7.0 Hip-Hop/Rap 0.0 106596.0 97588.0 77986.0 83681.0 74112.0 81261.0 87413.0 0.0 6938707.0 0.0 349.0 0.0 1165.0 1015.0 886.0 1076.0 782.0 877.0 1062.0 0.0 92919.0 0.0 11.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 263350.0 0.0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 22.0 2.0 0.0 9.0 0.0 8.0 11.0 3.0 0.0 4.0 12.0 15.0 0.0 0.0 0.0 0.0 0.0 6.0 7.0 7.0 8.0 2.0 2.0 4.0 4.0 1.0 3.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5576000.0 1460731.0 4423029.0 0.0 0.0 -41590706.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -10934.0 0.0 53.0 0.0 57.0 0.0 -11721.0 0.0
70568 41575962 You and I 9615450 You and I 2021-05-14 Paola Navarrete EC Ecuador 462889.0 R&B/Soul 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4476.0 -12.0 0.0 1.0 0.0 0.0 3144.0 -1.0 0.0 0.0 Hip-Hop/Rap 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5585.0 1627.0 1527.0 1322.0 807.0 1426.0 911.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
75093 15729592 Qué Manera de Quererte 3912668 Perdoname...¥Exitos! 2000-01-01 Gilberto Santa Rosa PR Puerto Rico 462904.0 Salsa 1 5279638.0 11602.0 0.0 16.0 1.0 85.0 1393051.0 2649.0 0.0 -1.0 31.0 88.0 -6175142.0 -13034.0 0.0 -20.0 -67.0 86.0 76719.0 368.0 0.0 7.0 116.0 48.0 Holiday 0.0 331906.0 394443.0 394340.0 490118.0 508593.0 0.0 27696.0 26466.0 28072.0 8315549.0 0.0 0.0 588.0 864.0 767.0 947.0 935.0 0.0 137.0 141.0 140.0 17256.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 6.0 0.0 0.0 0.0 5.0 17.0 9.0 0.0 78.0 0.0 5.0 102.0 9.0 0.0 28.0 33.0 29.0 29.0 30.0 34.0 27.0 25.0 21.0 18.0 9.0 0.0 -4782126.0 0.0 -2947599.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5079376.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 31.0 0.0 -2243.0 12.0 0.0 0.0
84241 13246762 Senza 'e te 770019 Fiorella 2014-11-20 Fiorella Mannoia IT Italy 462882.0 Pop 1 23923.0 3.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 -2.0 0.0 0.0 3465.0 27.0 0.0 2.0 0.0 0.0 Pop 0.0 0.0 0.0 0.0 0.0 0.0 0.0 693.0 1806.0 883.0 1796.0 786.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.0 16.0 7.0 10.0 10.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -43098215.0 0.0 0.0 0.0 -4793903.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
87988 15555431 MMMBop 5835080 Songs To Wash Your Hands To 2020-03-27 Hanson US United States 462882.0 Pop 1 6127773.0 48108.0 0.0 43.0 304.0 2683.0 41129.0 345.0 0.0 26.0 442.0 2529.0 -5883341.0 -44158.0 7485436.0 -1.0 -6311.0 499.0 -281804.0 -4293.0 132837844.0 3.0 6844.0 476.0 Pop 0.0 0.0 6107878.0 0.0 1380.0 5270818.0 14920.0 17449.0 0.0 5941341.0 0.0 0.0 0.0 0.0 47874.0 0.0 2.0 45188.0 119.0 188.0 0.0 44705.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2351826.0 2591363.0 2509306.0 2384767.0 2725217.0 126946865.0 3165762.0 0.0 0.0 43.0 0.0 26.0 1.0 0.0 0.0 0.0 2.0 0.0 1.0 0.0 0.0 216.0 175.0 141.0 126.0 95.0 0.0 5.0 0.0 6836.0 14.0 0.0 806.0 906.0 910.0 944.0 675.0 93.0 207.0 199.0 209.0 132.0 135.0 0.0 0.0 -127339411.0 0.0 0.0 0.0 0.0 0.0 0.0 6024830.0 0.0 0.0 6092398.0 0.0 6125870.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5338.0 0.0 3903.0
88385 14507008 You and I 1056433 In My Room 2016-07-01 Jacob Collier GB United Kingdom 462882.0 Pop 1 645090.0 10740.0 0.0 3.0 0.0 1.0 20209.0 368.0 0.0 -2.0 0.0 3.0 -630278.0 -11110.0 0.0 0.0 0.0 10.0 672753.0 11997.0 0.0 0.0 0.0 2.0 Jazz 0.0 0.0 610069.0 0.0 623797.0 6481.0 0.0 644378.0 0.0 658272.0 0.0 672753.0 0.0 0.0 10740.0 0.0 10979.0 130.0 0.0 11436.0 0.0 11727.0 0.0 11997.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 2.0 1.0 2.0 5.0 3.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -51632248.0 0.0 0.0 0.0 -161245653.0 0.0 0.0 606688.0 -16812158.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
92962 15117634 Money, Power & Respect (feat. DMX & Lil' Kim) 5559176 Hip Hop Klassiekers 2020-01-03 DMX US United States 462883.0 Hip-Hop/Rap 1 5836359.0 75510.0 0.0 56.0 10.0 139.0 1534214.0 20374.0 0.0 2.0 143.0 360.0 -7075657.0 -94912.0 1935343.0 0.0 -116.0 21.0 3893.0 45.0 1616468.0 1.0 67.0 27.0 Hip-Hop/Rap 0.0 204350.0 246818.0 855315.0 375498.0 303401.0 267491.0 0.0 1510.0 8405324.0 258663.0 0.0 0.0 2994.0 3166.0 13789.0 3467.0 3118.0 3042.0 0.0 23.0 110110.0 2723.0 0.0 0.0 0.0 0.0 0.0 0.0 518789.0 553172.0 763828.0 618343.0 606435.0 477898.0 532135.0 0.0 1.0 3.0 6.0 0.0 58.0 0.0 0.0 58.0 1.0 0.0 1.0 0.0 0.0 10.0 139.0 1.0 3.0 0.0 62.0 0.0 25.0 242.0 0.0 0.0 38.0 52.0 268.0 51.0 41.0 10.0 3.0 8.0 9.0 17.0 1.0 0.0 0.0 2913909.0 0.0 0.0 0.0 0.0 -137835.0 4975313.0 0.0 -28278.0 0.0 -782268.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -91.0 -1.0 0.0 0.0 0.0 -89.0 23.0 -23.0

Cluster 2 Country¶

In [109]:
df_youtube_c2_country = two.COUNTRY_NAME.value_counts()
df_youtube_c2_country = pd.DataFrame(data=df_youtube_c2_country)
df_youtube_c2_country.columns = ['count']
df_youtube_c2_country.plot.bar()
Out[109]:
<AxesSubplot:>

Cluster 2 Genre¶

In [110]:
df_youtube_c2 = two.ARTIST_GENRE.value_counts()
df_youtube_c2 = pd.DataFrame(data=df_youtube_c2)
df_youtube_c2.columns = ['count']
df_youtube_c2.plot.bar()
Out[110]:
<AxesSubplot:>
In [95]:
### Investigate cluster 3

df_3 = df_no_zero_youtube.query('cluster == 3')
list_3 = df_3.index.tolist()

three = df.query("TRACK_NAME in @list_3")
three
Out[95]:
CM_TRACK TRACK_NAME CM_ALBUM ALBUM_NAME RELEASE_DATE ARTIST_NAME ARTIST_COUNTRY_CODE2 COUNTRY_NAME GENRE_ID ARTIST_GENRE DUPE Q1_YOUTUBE_VIEWS_GROWTH Q1_YOUTUBE_LIKES_GROWTH Q1_SPOTIFY_PLAYS_GROWTH Q1_SPOTIFY_POPULARITY_GROWTH Q1_TIKTOK_POSTS_GROWTH Q1_AIRPLAY_STREAMS_GROWTH Q2_YOUTUBE_VIEWS_GROWTH Q2_YOUTUBE_LIKES_GROWTH Q2_SPOTIFY_PLAYS_GROWTH Q2_SPOTIFY_POPULARITY_GROWTH Q2_TIKTOK_POSTS_GROWTH Q2_AIRPLAY_STREAMS_GROWTH Q3_YOUTUBE_VIEWS_GROWTH Q3_YOUTUBE_LIKES_GROWTH Q3_SPOTIFY_PLAYS_GROWTH Q3_SPOTIFY_POPULARITY_GROWTH Q3_TIKTOK_POSTS_GROWTH Q3_AIRPLAY_STREAMS_GROWTH Q4_YOUTUBE_VIEWS_GROWTH Q4_YOUTUBE_LIKES_GROWTH Q4_SPOTIFY_PLAYS_GROWTH Q4_SPOTIFY_POPULARITY_GROWTH Q4_TIKTOK_POSTS_GROWTH Q4_AIRPLAY_STREAMS_GROWTH GENRE M1_YOUTUBE_VIEWS_GROWTH M2_YOUTUBE_VIEWS_GROWTH M3_YOUTUBE_VIEWS_GROWTH M4_YOUTUBE_VIEWS_GROWTH M5_YOUTUBE_VIEWS_GROWTH M6_YOUTUBE_VIEWS_GROWTH M7_YOUTUBE_VIEWS_GROWTH M8_YOUTUBE_VIEWS_GROWTH M9_YOUTUBE_VIEWS_GROWTH M10_YOUTUBE_VIEWS_GROWTH M11_YOUTUBE_VIEWS_GROWTH M12_YOUTUBE_VIEWS_GROWTH M1_YOUTUBE_LIKES_GROWTH M2_YOUTUBE_LIKES_GROWTH M3_YOUTUBE_LIKES_GROWTH M4_YOUTUBE_LIKES_GROWTH M5_YOUTUBE_LIKES_GROWTH M6_YOUTUBE_LIKES_GROWTH M7_YOUTUBE_LIKES_GROWTH M8_YOUTUBE_LIKES_GROWTH M9_YOUTUBE_LIKES_GROWTH M10_YOUTUBE_LIKES_GROWTH M11_YOUTUBE_LIKES_GROWTH M12_YOUTUBE_LIKES_GROWTH M1_SPOTIFY_PLAYS_GROWTH M2_SPOTIFY_PLAYS_GROWTH M3_SPOTIFY_PLAYS_GROWTH M4_SPOTIFY_PLAYS_GROWTH M5_SPOTIFY_PLAYS_GROWTH M6_SPOTIFY_PLAYS_GROWTH M7_SPOTIFY_PLAYS_GROWTH M8_SPOTIFY_PLAYS_GROWTH M9_SPOTIFY_PLAYS_GROWTH M10_SPOTIFY_PLAYS_GROWTH M11_SPOTIFY_PLAYS_GROWTH M12_SPOTIFY_PLAYS_GROWTH M1_SPOTIFY_POPULARITY_GROWTH M2_SPOTIFY_POPULARITY_GROWTH M3_SPOTIFY_POPULARITY_GROWTH M4_SPOTIFY_POPULARITY_GROWTH M5_SPOTIFY_POPULARITY_GROWTH M6_SPOTIFY_POPULARITY_GROWTH M7_SPOTIFY_POPULARITY_GROWTH M8_SPOTIFY_POPULARITY_GROWTH M9_SPOTIFY_POPULARITY_GROWTH M10_SPOTIFY_POPULARITY_GROWTH M11_SPOTIFY_POPULARITY_GROWTH M12_SPOTIFY_POPULARITY_GROWTH M1_TIKTOK_POSTS_GROWTH M2_TIKTOK_POSTS_GROWTH M3_TIKTOK_POSTS_GROWTH M4_TIKTOK_POSTS_GROWTH M5_TIKTOK_POSTS_GROWTH M6_TIKTOK_POSTS_GROWTH M7_TIKTOK_POSTS_GROWTH M8_TIKTOK_POSTS_GROWTH M9_TIKTOK_POSTS_GROWTH M10_TIKTOK_POSTS_GROWTH M11_TIKTOK_POSTS_GROWTH M12_TIKTOK_POSTS_GROWTH M1_AIRPLAY_STREAMS_GROWTH M2_AIRPLAY_STREAMS_GROWTH M3_AIRPLAY_STREAMS_GROWTH M4_AIRPLAY_STREAMS_GROWTH M5_AIRPLAY_STREAMS_GROWTH M6_AIRPLAY_STREAMS_GROWTH M7_AIRPLAY_STREAMS_GROWTH M8_AIRPLAY_STREAMS_GROWTH M9_AIRPLAY_STREAMS_GROWTH M10_AIRPLAY_STREAMS_GROWTH M11_AIRPLAY_STREAMS_GROWTH M12_AIRPLAY_STREAMS_GROWTH WK1_YT_VIEWS_GROWTH WK2_YT_VIEWS_GROWTH WK3_YT_VIEWS_GROWTH WK4_YT_VIEWS_GROWTH WK5_YT_VIEWS_GROWTH WK6_YT_VIEWS_GROWTH WK7_YT_VIEWS_GROWTH WK8_YT_VIEWS_GROWTH WK9_YT_VIEWS_GROWTH WK10_YT_VIEWS_GROWTH WK11_YT_VIEWS_GROWTH WK12_YT_VIEWS_GROWTH WK13_YT_VIEWS_GROWTH WK14_YT_VIEWS_GROWTH WK15_YT_VIEWS_GROWTH WK1_SPOTIFY_PLAYS_GROWTH WK2_SPOTIFY_PLAYS_GROWTH WK3_SPOTIFY_PLAYS_GROWTH WK4_SPOTIFY_PLAYS_GROWTH WK5_SPOTIFY_PLAYS_GROWTH WK6_SPOTIFY_PLAYS_GROWTH WK7_SPOTIFY_PLAYS_GROWTH WK8_SPOTIFY_PLAYS_GROWTH WK9_SPOTIFY_PLAYS_GROWTH WK10_SPOTIFY_PLAYS_GROWTH WK11_SPOTIFY_PLAYS_GROWTH WK12_SPOTIFY_PLAYS_GROWTH WK13_SPOTIFY_PLAYS_GROWTH WK14_SPOTIFY_PLAYS_GROWTH WK15_SPOTIFY_PLAYS_GROWTH WK1_TIKTOK_POSTS_GROWTH WK2_TIKTOK_POSTS_GROWTH WK3_TIKTOK_POSTS_GROWTH WK4_TIKTOK_POSTS_GROWTH WK5_TIKTOK_POSTS_GROWTH WK6_TIKTOK_POSTS_GROWTH WK7_TIKTOK_POSTS_GROWTH WK8_TIKTOK_POSTS_GROWTH WK9_TIKTOK_POSTS_GROWTH WK10_TIKTOK_POSTS_GROWTH WK11_TIKTOK_POSTS_GROWTH WK12_TIKTOK_POSTS_GROWTH WK13_TIKTOK_POSTS_GROWTH WK14_TIKTOK_POSTS_GROWTH WK15_TIKTOK_POSTS_GROWTH
11168 12126263 Raelsan 96312 Le chant des sirĂšnes 2011-09-26 Orelsan FR France 462883.0 Hip-Hop/Rap 1 10747972.0 56445.0 0.0 43.0 0.0 3.0 138389.0 1283.0 0.0 -1.0 0.0 2.0 133774.0 1325.0 0.0 -40.0 3.0 1.0 -10238460.0 -47704.0 0.0 5.0 8.0 98.0 Hip-Hop/Rap 0.0 47377.0 53620.0 49471.0 0.0 10141958.0 45100.0 44021.0 44653.0 0.0 10729038.0 0.0 0.0 355.0 458.0 452.0 0.0 44818.0 388.0 414.0 523.0 0.0 51402.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 43.0 0.0 0.0 0.0 0.0 1.0 0.0 2.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 1.0 45.0 35.0 18.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -142771.0 -2499727.0 0.0 0.0 0.0 0.0 10718853.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
14364 13277275 Bolla Papale Freestyle 1424676 MM Vol. 2 2016-11-11 MadMan US United States 462883.0 Hip-Hop/Rap 1 9620109.0 128656.0 0.0 1.0 0.0 0.0 72302.0 870.0 0.0 -1.0 0.0 0.0 77561.0 903.0 0.0 0.0 0.0 0.0 -9767146.0 0.0 0.0 0.0 0.0 0.0 Hip-Hop/Rap 0.0 24358.0 25988.0 0.0 0.0 9689603.0 25197.0 28846.0 23518.0 25605.0 35939.0 0.0 0.0 305.0 306.0 0.0 0.0 0.0 309.0 355.0 239.0 311.0 621.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 270605.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 4.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -6005567.0 0.0 0.0 0.0 0.0 4612259.0 0.0 -61102770.0 -4694131.0 8944102.0 0.0 0.0 8036864.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
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17628 15090581 Left Right Left 3959753 Nine Track Mind Deluxe 2016-11-11 Charlie Puth US United States 462882.0 Pop 1 8439283.0 48437.0 0.0 2.0 899.0 11.0 142041.0 1057.0 0.0 0.0 -1.0 12.0 128090.0 964.0 0.0 -16.0 0.0 6.0 -8704585.0 -50451.0 402796.0 2.0 0.0 12.0 Pop 0.0 44792.0 54969.0 46586.0 0.0 8576689.0 45345.0 43205.0 39540.0 49789.0 41972.0 0.0 0.0 337.0 383.0 298.0 0.0 49490.0 336.0 309.0 319.0 389.0 273.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 79370.0 167236.0 119536.0 116024.0 0.0 0.0 2.0 0.0 7.0 0.0 0.0 0.0 0.0 10.0 0.0 0.0 0.0 898.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 860.0 0.0 0.0 3.0 6.0 4.0 5.0 3.0 1.0 1.0 4.0 5.0 5.0 2.0 0.0 0.0 5249591.0 7954391.0 0.0 0.0 0.0 -484053654.0 7894895.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -18297.0 0.0 0.0 0.0 0.0 0.0 0.0 -486.0
31016 11646564 YouÂŽre Not Alone 135506 Seven Years 2005-06-13 ATB DE Germany 462927.0 Trance 1 9157383.0 35266.0 0.0 5.0 0.0 171.0 163114.0 1354.0 0.0 -1.0 0.0 179.0 121154.0 1182.0 0.0 0.0 0.0 0.0 -9435154.0 -37782.0 0.0 0.0 8.0 85.0 Dance 0.0 64319.0 73603.0 61121.0 0.0 9315263.0 44860.0 40893.0 35401.0 35537.0 39285.0 0.0 0.0 600.0 621.0 429.0 0.0 36615.0 411.0 390.0 381.0 373.0 389.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 9.0 0.0 55.0 47.0 54.0 76.0 49.0 0.0 0.0 0.0 0.0 2.0 83.0 0.0 0.0 0.0 0.0 2253022.0 2085667.0 0.0 0.0 0.0 9081142.0 0.0 196763.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
41331 11084402 A Viagem 3102376 Vidavida 1994-08-28 Roupa Nova BR Brazil 462877.0 Sertanejo 1 13349190.0 104266.0 0.0 32.0 1623.0 24.0 105767.0 1133.0 0.0 -18.0 -1434.0 26.0 628560.0 6970.0 0.0 -3.0 -130.0 26.0 -11938541.0 -87346.0 0.0 3.0 150.0 41.0 Pop 0.0 0.0 0.0 0.0 0.0 12067614.0 181423.0 0.0 14026930.0 258436.0 0.0 101310.0 0.0 0.0 0.0 0.0 0.0 88981.0 2021.0 0.0 112370.0 2610.0 0.0 1188.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 165519.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 25.0 0.0 154.0 1468.0 0.0 1729.0 0.0 0.0 44.0 39.0 0.0 0.0 243.0 0.0 5.0 9.0 10.0 7.0 9.0 3.0 10.0 13.0 17.0 15.0 9.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -2298842.0 -3456791.0 0.0 0.0 -1604565.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 134.0 1708.0
44563 12588374 Sing It Back - Boris Dlugosch Mix 8026903 My Kitchen Rules 2012-01-01 Boris Dlugosch DE Germany 462890.0 House 1 12150213.0 63290.0 0.0 65.0 54.0 2578.0 0.0 -60.0 0.0 0.0 168.0 2814.0 0.0 -29.0 4052861.0 -2.0 -539.0 284.0 -11562091.0 -54835.0 4099523.0 3.0 1019.0 165.0 Dance 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12073850.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 62640.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1416690.0 1435676.0 1346098.0 1271087.0 1321401.0 1272780.0 1505342.0 0.0 1.0 2.0 0.0 0.0 27.0 0.0 0.0 0.0 2.0 0.0 1.0 0.0 0.0 54.0 66.0 49.0 53.0 0.0 25.0 0.0 0.0 744.0 276.0 0.0 783.0 883.0 957.0 981.0 876.0 83.0 113.0 88.0 66.0 51.0 48.0 0.0 0.0 0.0 0.0 0.0 10993961.0 0.0 0.0 5731942.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -771.0 0.0 0.0 0.0 0.0 0.0 0.0 307.0 0.0 0.0 -42726.0 393.0 0.0
49950 15815593 John Deere Green 181823 The Essential Joe Diffie 2008-07-08 Joe Diffie US United States 462895.0 Country 1 6183185.0 43788.0 0.0 3.0 0.0 512.0 1049399.0 6609.0 0.0 35.0 0.0 425.0 894199.0 5337.0 4780490.0 1.0 0.0 364.0 755300.0 4652.0 3212346.0 3.0 0.0 353.0 Country 0.0 287868.0 308456.0 363465.0 380886.0 305048.0 342176.0 284981.0 267042.0 276224.0 249830.0 229246.0 0.0 2174.0 2103.0 2227.0 2516.0 1866.0 2162.0 1682.0 1493.0 1718.0 1528.0 1406.0 0.0 0.0 0.0 0.0 0.0 1778201.0 1851673.0 1537177.0 1391640.0 1171651.0 1061246.0 979449.0 0.0 0.0 0.0 33.0 1.0 1.0 0.0 0.0 38.0 3.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 241.0 154.0 148.0 137.0 140.0 125.0 94.0 145.0 186.0 98.0 69.0 0.0 0.0 0.0 0.0 2764451.0 0.0 0.0 5290548.0 0.0 5893046.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
58949 13044497 It Ain't Safe 654060 It Ain't Safe (feat. Young Lord) 2014-11-02 Young L.O.R.D. US United States 462889.0 R&B/Soul 1 10064727.0 103088.0 0.0 57.0 57.0 7.0 320318.0 3709.0 0.0 -1.0 52.0 12.0 355076.0 3978.0 0.0 -1.0 -299.0 14.0 -9957411.0 -103770.0 0.0 3.0 411.0 1.0 Hip-Hop/Rap 0.0 145683.0 134290.0 108900.0 113421.0 97997.0 111785.0 130152.0 113139.0 0.0 10197058.0 0.0 0.0 1667.0 1427.0 1240.0 1447.0 1022.0 1097.0 1443.0 1438.0 0.0 106347.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 43.0 1.0 0.0 2.0 0.0 27.0 29.0 19.0 30.0 3.0 0.0 29.0 0.0 0.0 0.0 412.0 0.0 1.0 1.0 4.0 8.0 0.0 5.0 3.0 6.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -11355988.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -161421121.0 -10503749.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 209.0 0.0 58.0 0.0 0.0 0.0 0.0
60636 18263822 Feed the Wolf 5677900 Antrenament Ăźn ritm ROCK 2020-02-07 Breaking Benjamin US United States 462898.0 Metal 1 9116709.0 79687.0 0.0 56.0 7.0 0.0 156959.0 997.0 0.0 -1.0 34.0 0.0 139795.0 926.0 0.0 -1.0 17.0 0.0 -9409151.0 -81600.0 0.0 1.0 -85.0 0.0 Hard Rock 0.0 62758.0 67163.0 52646.0 0.0 9269867.0 49350.0 48286.0 42159.0 46908.0 46397.0 0.0 0.0 416.0 416.0 273.0 0.0 80681.0 322.0 301.0 303.0 296.0 316.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 56.0 0.0 0.0 0.0 0.0 0.0 54.0 1.0 0.0 0.0 0.0 0.0 0.0 5.0 14.0 15.0 0.0 0.0 0.0 4.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3179720.0 0.0 6360227.0 7886131.0 -186752521.0 0.0 -121248313.0 0.0 0.0 0.0 6831261.0 8955636.0 0.0 -67396153.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 58.0 0.0 0.0 0.0 -108.0
64996 20320324 A Viagem 5407120 ColigaçÔes Expressivas 5 2019-11-22 Delacruz BR Brazil 462882.0 Pop 1 0.0 0.0 0.0 40.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 129633.0 -16.0 0.0 0.0 0.0 0.0 115112.0 2.0 0.0 0.0 Hip-Hop/Rap 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 49658.0 46590.0 42957.0 40086.0 40428.0 31148.0 43536.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 1.0 0.0 2.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
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67017 15533794 Before The Next Teardrop Falls 10080226 At His Best 2017-02-17 Freddy Fender US United States 462895.0 Country 1 18649702.0 80681.0 0.0 47.0 599.0 7.0 285961.0 2461.0 0.0 1.0 127.0 12.0 921379.0 9896.0 465127.0 -48.0 -724.0 107.0 -9326339.0 -11624.0 475290.0 1.0 862.0 80.0 Country 0.0 0.0 13048975.0 0.0 583671.0 11697339.0 0.0 11338875.0 313214.0 233313.0 183351.0 0.0 0.0 0.0 39505.0 0.0 4675.0 29772.0 0.0 28100.0 3352.0 2295.0 1618.0 0.0 0.0 0.0 0.0 0.0 0.0 136161.0 132625.0 166569.0 165933.0 167152.0 151611.0 156527.0 0.0 0.0 2.0 0.0 1.0 1.0 0.0 0.0 0.0 18.0 1.0 0.0 0.0 51.0 60.0 28.0 60.0 39.0 0.0 0.0 1.0 2.0 858.0 2.0 0.0 1.0 1.0 0.0 0.0 12.0 40.0 39.0 28.0 29.0 33.0 18.0 0.0 0.0 -26472206.0 15868197.0 0.0 4184206.0 0.0 0.0 0.0 0.0 4990588.0 0.0 16274892.0 4291958.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -411.0 0.0 0.0 0.0 481.0 -7549.0 0.0 0.0 -193.0 0.0 0.0 611.0
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74631 12471868 A Night To Remember 17060505 A Night To Remember 2013-06-28 Shalamar US United States 462889.0 R&B/Soul 1 9941035.0 78496.0 0.0 28.0 121.0 1110.0 524586.0 5724.0 0.0 20.0 1350.0 1244.0 562338.0 5777.0 0.0 -1.0 56.0 1627.0 -10729760.0 -89649.0 0.0 -1.0 120.0 1118.0 R&B/Soul 0.0 2532515.0 212668.0 0.0 10261771.0 172108.0 177196.0 200923.0 184219.0 0.0 11125218.0 0.0 0.0 24630.0 2128.0 0.0 83873.0 1926.0 1886.0 2192.0 1699.0 0.0 93648.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 24.0 0.0 20.0 1.0 0.0 0.0 41.0 2.0 0.0 0.0 0.0 55.0 35.0 27.0 33.0 1290.0 12.0 6.0 38.0 0.0 0.0 0.0 0.0 336.0 379.0 400.0 423.0 421.0 724.0 510.0 393.0 407.0 336.0 375.0 -9833244.0 0.0 -151270.0 -9316419.0 5229079.0 0.0 0.0 -10925214.0 0.0 0.0 -242256030.0 0.0 -162550232.0 -9746483.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -14406.0 0.0 -10790.0 -3253.0 0.0
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81194 15717316 Resolve 1840280 In Your Honor 2005-06-14 Foo Fighters US United States 462882.0 Pop 1 11572091.0 32436.0 0.0 45.0 0.0 31.0 227770.0 1087.0 0.0 -1.0 0.0 35.0 210660.0 1027.0 0.0 -44.0 0.0 73.0 -11660351.0 -33663.0 0.0 0.0 0.0 88.0 Alternative 0.0 81514.0 84737.0 74172.0 90486.0 63112.0 77268.0 71915.0 61477.0 66104.0 76867.0 0.0 0.0 380.0 404.0 347.0 443.0 297.0 386.0 324.0 317.0 296.0 387.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 45.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11.0 8.0 16.0 10.0 9.0 13.0 34.0 26.0 34.0 28.0 26.0 0.0 -10343994.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11358918.0 -57857432.0 3018120.0 968820.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
82604 13248590 Il bene 535159 ScriverĂČ il tuo nome (Deluxe Edition) 2016-04-15 Francesco Renga IT Italy 462882.0 Pop 1 9717953.0 40576.0 0.0 7.0 465.0 14.0 251305.0 902.0 0.0 -3.0 165.0 8.0 147709.0 764.0 299737.0 0.0 67.0 10.0 -10113267.0 -42239.0 342394.0 0.0 162.0 10.0 Alternative 0.0 172429.0 191238.0 113551.0 89047.0 48707.0 44040.0 53591.0 50078.0 66638.0 55045.0 0.0 0.0 1176.0 970.0 415.0 302.0 185.0 207.0 273.0 284.0 322.0 274.0 0.0 0.0 0.0 0.0 0.0 0.0 102314.0 89151.0 109748.0 100838.0 121175.0 105636.0 115583.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 269.0 102.0 48.0 15.0 0.0 38.0 33.0 67.0 59.0 36.0 0.0 4.0 3.0 3.0 4.0 1.0 4.0 3.0 3.0 4.0 3.0 3.0 0.0 0.0 0.0 -4465392.0 5788528.0 0.0 0.0 0.0 0.0 0.0 9599599.0 5920242.0 4711862.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 635.0 0.0 -91782.0 0.0 0.0 -600.0 1091.0 0.0 0.0
87816 15763460 Lady Marmalade 1025073 Kiss FM. Vive la Mejor MĂșsica de Todos los Tie... 2014-11-24 Patti LaBelle US United States 462889.0 R&B/Soul 1 8208174.0 77642.0 0.0 58.0 32.0 0.0 251017.0 4342.0 0.0 -1.0 17.0 22.0 326383.0 6885.0 2657357.0 -2.0 -87.0 335.0 -8781973.0 -88861.0 2622223.0 1.0 114.0 347.0 R&B/Soul 0.0 71965.0 94592.0 0.0 7660552.0 87428.0 108231.0 101819.0 116333.0 84980.0 71843.0 0.0 0.0 1101.0 1592.0 0.0 75418.0 1783.0 2656.0 2229.0 2000.0 1526.0 1066.0 0.0 0.0 0.0 0.0 0.0 0.0 843858.0 921538.0 873863.0 861956.0 930960.0 873451.0 817812.0 0.0 0.0 47.0 0.0 0.0 0.0 0.0 0.0 43.0 1.0 0.0 0.0 0.0 0.0 0.0 6.0 6.0 5.0 0.0 98.0 0.0 5.0 3.0 106.0 0.0 0.0 0.0 0.0 0.0 22.0 112.0 108.0 115.0 102.0 112.0 133.0 -29090215.0 0.0 0.0 0.0 7736644.0 -73686800.0 0.0 0.0 0.0 -48161864.0 -90578662.0 0.0 0.0 0.0 4085148.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -45.0 0.0 0.0 -42769.0 121.0 0.0 -682.0 -204.0
91692 15843518 Down in the Valley 4005880 The Head and the Heart 2010-01-01 The Head And The Heart US United States 462882.0 Pop 1 10033090.0 56101.0 0.0 60.0 13.0 22.0 170180.0 1211.0 0.0 -1.0 29.0 22.0 167975.0 1564.0 0.0 -8.0 -179.0 17.0 -10364967.0 -58868.0 0.0 -1.0 3.0 23.0 Alternative 0.0 59531.0 67911.0 56778.0 65509.0 47893.0 51455.0 56846.0 59674.0 62600.0 54049.0 0.0 0.0 389.0 472.0 340.0 535.0 336.0 537.0 573.0 454.0 448.0 399.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 589673.0 0.0 29.0 3.0 0.0 0.0 0.0 0.0 0.0 30.0 0.0 50.0 0.0 0.0 0.0 8.0 3.0 11.0 15.0 0.0 16.0 0.0 0.0 0.0 0.0 0.0 6.0 5.0 9.0 8.0 5.0 5.0 6.0 6.0 6.0 8.0 9.0 0.0 -9247808.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -494383923.0 0.0 0.0 2452030.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 141.0 0.0 0.0 0.0 32.0 146.0 0.0
92198 15815646 John Deere Green 3953763 Honky Tonk Attitude 1993-04-20 Joe Diffie US United States 462895.0 Country 1 9050732.0 34479.0 0.0 4.0 1227.0 510.0 128628.0 871.0 0.0 1.0 973.0 391.0 97107.0 678.0 0.0 -5.0 983.0 1.0 -9271143.0 -36005.0 0.0 -3.0 759.0 1.0 Country 0.0 38944.0 49931.0 45271.0 0.0 9174841.0 0.0 9239958.0 28065.0 31129.0 24758.0 0.0 0.0 290.0 331.0 304.0 0.0 35343.0 0.0 35755.0 190.0 256.0 177.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 368.0 404.0 282.0 390.0 301.0 317.0 313.0 353.0 357.0 234.0 168.0 0.0 241.0 152.0 149.0 138.0 104.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 8228140.0 0.0 0.0 0.0 0.0 0.0 -633046.0 -9527055.0 8729984.0 -6075022.0 -815775.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -4978.0 0.0 0.0 5121.0 0.0 0.0 0.0 5466.0 3744.0 0.0 5131.0 5583.0 0.0 0.0
94922 13250427 La donna cannone 3911484 La Donna Cannone 1983-01-01 Francesco De Gregori IT Italy 462884.0 Rock 1 17344290.0 53891.0 0.0 58.0 0.0 125.0 657634.0 2926.0 0.0 0.0 0.0 135.0 527329.0 2455.0 1464351.0 -2.0 0.0 102.0 -11418802.0 -17637.0 1266778.0 1.0 0.0 123.0 Pop 0.0 228258.0 243892.0 0.0 17817511.0 141338.0 167272.0 197446.0 162611.0 208059.0 0.0 129403.0 0.0 801.0 972.0 0.0 55994.0 642.0 767.0 960.0 728.0 898.0 0.0 702.0 0.0 0.0 0.0 0.0 0.0 512366.0 483930.0 539732.0 440689.0 432836.0 427431.0 406511.0 0.0 0.0 34.0 0.0 0.0 0.0 0.0 0.0 31.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 40.0 38.0 54.0 40.0 41.0 31.0 37.0 34.0 39.0 38.0 46.0 -1019162.0 15698797.0 0.0 10667636.0 0.0 0.0 0.0 0.0 0.0 17121084.0 0.0 -199242968.0 -9941924.0 0.0 17434874.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Cluster 3 Country¶

In [103]:
df_youtube_c3_country = three.COUNTRY_NAME.value_counts()
df_youtube_c3_country = pd.DataFrame(data=df_youtube_c2_country)
df_youtube_c3_country.columns = ['count']
df_youtube_c3_country.plot.bar()
Out[103]:
<AxesSubplot:>

Cluster 3 Genre¶

In [102]:
df_youtube_c3_genre = three.ARTIST_GENRE.value_counts()
df_youtube_c3_genre = pd.DataFrame(data=df_youtube_c3_genre)
df_youtube_c3_genre.columns = ['count']
df_youtube_c3_genre.plot.bar()
Out[102]:
<AxesSubplot:>

DBscan Modeling - Spotify¶

Repeat exactly the same thing as we did previously. Optimize params > build model with optimized params > remove 0 growth > optimize params > build new model > evaluate

In [73]:
### Slice out WoW Columns 

qoq_spotify = df_sample_scaled[["Q1_SPOTIFY_PLAYS_GROWTH", "Q2_SPOTIFY_PLAYS_GROWTH", "Q3_SPOTIFY_PLAYS_GROWTH",
                       "Q4_SPOTIFY_PLAYS_GROWTH"]]
Grid search for optimum clusters (not running since we know clusters around 4-6 is optimal and great of biz insights)¶
In [74]:
from sklearn.cluster import KMeans 
from sklearn.metrics import silhouette_samples, silhouette_score 
import time 

n_clusters = [3,4,5,6]

for i in n_clusters:
    start = time.time()
    clusterer = KMeans(n_clusters = i, random_state = 10)
    cluster_labels = clusterer.fit_predict(qoq_spotify)
    silhouette_avg = silhouette_score(qoq_spotify, cluster_labels)
    end = time.time()
    timetaken = end - start
    print("For n_clusters =", i, "The average silhouette score is:", silhouette_avg)
    print("This iteration took", timetaken)
    sample_silhouette_values = silhouette_samples(qoq_youtube, cluster_labels)
For n_clusters = 3 The average silhouette score is: 0.9967500853243395
This iteration took 21.889666080474854
For n_clusters = 4 The average silhouette score is: 0.9941777959767116
This iteration took 21.822834968566895
For n_clusters = 5 The average silhouette score is: 0.9935838383820632
This iteration took 21.408591270446777
For n_clusters = 6 The average silhouette score is: 0.9748434589216639
This iteration took 21.693091869354248
Determine optimum EPS¶
In [75]:
### First, determine EPS range 

from sklearn.neighbors import NearestNeighbors
from matplotlib import pyplot as plt


neighbors = NearestNeighbors(n_neighbors=8)
neighbors_fit = neighbors.fit(qoq_spotify)
distances, indices = neighbors_fit.kneighbors(qoq_spotify)

distances = np.sort(distances, axis=0)
distances = distances[:,1]
plt.axis([0, 51000, 0, 0.02])
plt.plot(distances)
Out[75]:
[<matplotlib.lines.Line2D at 0x7f8a192090a0>]
In [76]:
### Determine Optimum EPS 
from sklearn.cluster import DBSCAN    
    
eps_range = [0.001, 0.002, 0.003, 0.004, 0.005]

for i in eps_range:
    start = time.time()
    print("eps value is" + str(i))
    db = DBSCAN(eps = i, min_samples=8).fit(qoq_spotify)
    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
    core_samples_mask[db.core_sample_indices_] = True 
    labels = db.labels_
    silhouette_avg = silhouette_score(qoq_spotify, labels)
    print(set(labels))
    silhouette_avg = silhouette_score(qoq_spotify, labels)
    end = time.time()
    timetaken = end - start
    print("For eps value ="+ str(i), labels, "the average silhouette score is:", silhouette_avg)
    print("This iteration took", timetaken, "seconds")
    
eps value is0.001
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, -1}
For eps value =0.001 [ 0 -1  0 ...  0  0  0] the average silhouette score is: 0.15876002626427932
This iteration took 61.006664991378784 seconds
eps value is0.002
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, -1}
For eps value =0.002 [ 0 -1  0 ...  0  0  0] the average silhouette score is: 0.2918826649922619
This iteration took 60.876980781555176 seconds
eps value is0.003
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, -1}
For eps value =0.003 [ 0 -1  0 ...  0  0  0] the average silhouette score is: 0.32459875798168575
This iteration took 62.36633014678955 seconds
eps value is0.004
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, -1}
For eps value =0.004 [ 0 -1  0 ...  0  0  0] the average silhouette score is: 0.421031849056015
This iteration took 63.780704975128174 seconds
eps value is0.005
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, -1}
For eps value =0.005 [ 0 -1  0 ...  0  0  0] the average silhouette score is: 0.5022656507002081
This iteration took 62.979327917099 seconds
Determine min_samples¶
In [77]:
min_samples = [24,25,26,27,28]

for i in min_samples: 
    start = time.time()
    db = DBSCAN(eps = 0.004, min_samples=i).fit(qoq_spotify)
    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
    core_samples_mask[db.core_sample_indices_] = True 
    labels = db.labels_
    silhouette_avg = silhouette_score(qoq_spotify, labels)
    end = time.time()
    timetaken = end - start
    
    print("For min_sample value =" +str(i), "clusters produced ="+ str(len(set(labels))))
    print("This iteration took", timetaken, "seconds")
For min_sample value =24 clusters produced =9
This iteration took 41.484431982040405 seconds
For min_sample value =25 clusters produced =11
This iteration took 40.71910285949707 seconds
For min_sample value =26 clusters produced =11
This iteration took 40.97695708274841 seconds
For min_sample value =27 clusters produced =8
This iteration took 41.1752188205719 seconds
For min_sample value =28 clusters produced =8
This iteration took 40.77605080604553 seconds
Build model with optimum parameters¶
In [78]:
clustering = DBSCAN(eps=0.004, min_samples=28).fit(qoq_spotify)
components = clustering.components_
labels =clustering.labels_
core_sample_indices = clustering.core_sample_indices_                      

qoq_spotify.insert(0, 'cluster',clustering.labels_)
### count values within clusters 
qoq_spotify.cluster.value_counts()
Out[78]:
 0    42752
-1     5669
 1      178
 2      119
 4       61
 5       35
 3       32
 6       28
Name: cluster, dtype: int64
Remove cluster 0¶
In [79]:
### slice out the data we want 
df_no_zero_spotify = qoq_spotify.query('cluster == -1' or 'cluster == 1' or 'cluster == 2' or 'cluster == 3' or 'cluster == 4')
### drop cluster column
df_no_zero_spotify = df_no_zero_spotify.drop(['cluster'], axis = 1)
Optimize parameters again for new YouTube model¶
In [80]:
### Determine optimum Epsilon range

from sklearn.neighbors import NearestNeighbors
from matplotlib import pyplot as plt


neighbors = NearestNeighbors(n_neighbors=8)
neighbors_fit = neighbors.fit(df_no_zero_spotify)
distances, indices = neighbors_fit.kneighbors(df_no_zero_spotify)

distances = np.sort(distances, axis=0)
distances = distances[:,1]
plt.axis([0, 6400, 0, 1])
plt.plot(distances)
Out[80]:
[<matplotlib.lines.Line2D at 0x7f90f80c0e50>]
In [81]:
### Determine optimum EPS 

eps_range = [0.05, 0.1, 0.15, 0.2, 0.25, 0.3]

for i in eps_range:
    start = time.time()
    print("eps value is" + str(i))
    db = DBSCAN(eps = i, min_samples=8).fit(df_no_zero_spotify)
    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
    core_samples_mask[db.core_sample_indices_] = True 
    labels = db.labels_
    silhouette_avg = silhouette_score(df_no_zero_spotify, labels)
    print(set(labels))
    silhouette_avg = silhouette_score(df_no_zero_spotify, labels)
    end = time.time()
    timetaken = start - end
    print("For eps value ="+ str(i), labels, "the average silhouette score is:", silhouette_avg)
    print("This iteration took", timetaken)
eps value is0.05
{0, 1, 2, 3, -1}
For eps value =0.05 [0 0 0 ... 0 0 0] the average silhouette score is: 0.4993567007493581
This iteration took -0.767949104309082
eps value is0.1
{0, 1, 2, 3, 4, 5, 6, -1}
For eps value =0.1 [0 0 0 ... 0 0 0] the average silhouette score is: -0.005111500251722316
This iteration took -0.667341947555542
eps value is0.15
{0, 1, 2, 3, 4, -1}
For eps value =0.15 [0 0 0 ... 0 0 0] the average silhouette score is: 0.4948063068861464
This iteration took -0.6944882869720459
eps value is0.2
{0, 1, 2, 3, 4, -1}
For eps value =0.2 [0 0 0 ... 0 0 0] the average silhouette score is: 0.6340161331143919
This iteration took -0.7002310752868652
eps value is0.25
{0, 1, 2, 3, 4, -1}
For eps value =0.25 [0 0 0 ... 0 0 0] the average silhouette score is: 0.6969086195838394
This iteration took -0.7103168964385986
eps value is0.3
{0, 1, 2, 3, 4, -1}
For eps value =0.3 [0 0 0 ... 0 0 0] the average silhouette score is: 0.6922544906937437
This iteration took -0.7114670276641846
In [82]:
### Determine optimum min_samples

min_samples = [7,8,9,10,11,12,13, 14, 15]

for i in min_samples: 
    db = DBSCAN(eps = 0.3, min_samples=i).fit(df_no_zero_spotify)
    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
    core_samples_mask[db.core_sample_indices_] = True 
    labels = db.labels_
    silhouette_avg = silhouette_score(df_no_zero_spotify, labels)
    
    print("For min_sample value =" +str(i), "clusters produced ="+ str(len(set(labels))))
For min_sample value =7 clusters produced =8
For min_sample value =8 clusters produced =6
For min_sample value =9 clusters produced =5
For min_sample value =10 clusters produced =4
For min_sample value =11 clusters produced =5
For min_sample value =12 clusters produced =5
For min_sample value =13 clusters produced =3
For min_sample value =14 clusters produced =3
For min_sample value =15 clusters produced =3
Build new model with optimum parameters¶
In [83]:
clustering = DBSCAN(eps=0.3, min_samples=9).fit(df_no_zero_spotify)
components = clustering.components_
labels =clustering.labels_
core_sample_indices = clustering.core_sample_indices_                      

df_no_zero_spotify.insert(0, 'cluster',clustering.labels_)
### count values within clusters 
df_no_zero_spotify.cluster.value_counts()
Out[83]:
 0    5429
-1     202
 1      16
 3      13
 2       9
Name: cluster, dtype: int64
In [84]:
clusters = df_no_zero_spotify \
    .groupby('cluster') \
    .agg('mean')

import matplotlib.pyplot as plt
from matplotlib import colors

def background_gradient(s, m, M, cmap='PuBu', low=0, high=0):
    rng = M - m
    norm = colors.Normalize(m - (rng * low),
                            M + (rng * high))
    normed = norm(s.values)
    c = [colors.rgb2hex(x) for x in plt.cm.get_cmap(cmap)(normed)]
    return ['background-color: %s' % color for color in c]

clusters.style.apply(background_gradient,
               cmap='Wistia',
               m=clusters.min().min(),
               M=clusters.max().max())
Out[84]:
  Q1_SPOTIFY_PLAYS_GROWTH Q2_SPOTIFY_PLAYS_GROWTH Q3_SPOTIFY_PLAYS_GROWTH Q4_SPOTIFY_PLAYS_GROWTH
cluster        
-1 6.166798 3.512660 2.898341 8.340313
0 -0.008941 -0.014578 0.186263 0.502784
1 2.963808 -0.014578 -0.040787 0.526614
2 -0.028792 -0.014578 3.078321 7.074816
3 -0.028792 -0.014578 2.856379 6.077754
In [85]:
import plotly.express as px

fig = px.scatter_3d(df_no_zero_spotify, x="Q1_SPOTIFY_PLAYS_GROWTH", y="Q2_SPOTIFY_PLAYS_GROWTH", z= 'Q3_SPOTIFY_PLAYS_GROWTH',
              color='cluster')
fig.show()
fig.update_layout(margin=dict(l=0, r=0, b=0, t=0))
In [104]:
### Investigate cluster -1 

df_negative_1_s = df_no_zero_spotify.query('cluster == -1')
list_negative_1 = df_negative_1.index.tolist()

negative_1_s = df.query("TRACK_NAME in @list_negative_1")
negative_1_s
Out[104]:
CM_TRACK TRACK_NAME CM_ALBUM ALBUM_NAME RELEASE_DATE ARTIST_NAME ARTIST_COUNTRY_CODE2 COUNTRY_NAME GENRE_ID ARTIST_GENRE DUPE Q1_YOUTUBE_VIEWS_GROWTH Q1_YOUTUBE_LIKES_GROWTH Q1_SPOTIFY_PLAYS_GROWTH Q1_SPOTIFY_POPULARITY_GROWTH Q1_TIKTOK_POSTS_GROWTH Q1_AIRPLAY_STREAMS_GROWTH Q2_YOUTUBE_VIEWS_GROWTH Q2_YOUTUBE_LIKES_GROWTH Q2_SPOTIFY_PLAYS_GROWTH Q2_SPOTIFY_POPULARITY_GROWTH Q2_TIKTOK_POSTS_GROWTH Q2_AIRPLAY_STREAMS_GROWTH Q3_YOUTUBE_VIEWS_GROWTH Q3_YOUTUBE_LIKES_GROWTH Q3_SPOTIFY_PLAYS_GROWTH Q3_SPOTIFY_POPULARITY_GROWTH Q3_TIKTOK_POSTS_GROWTH Q3_AIRPLAY_STREAMS_GROWTH Q4_YOUTUBE_VIEWS_GROWTH Q4_YOUTUBE_LIKES_GROWTH Q4_SPOTIFY_PLAYS_GROWTH Q4_SPOTIFY_POPULARITY_GROWTH Q4_TIKTOK_POSTS_GROWTH Q4_AIRPLAY_STREAMS_GROWTH GENRE M1_YOUTUBE_VIEWS_GROWTH M2_YOUTUBE_VIEWS_GROWTH M3_YOUTUBE_VIEWS_GROWTH M4_YOUTUBE_VIEWS_GROWTH M5_YOUTUBE_VIEWS_GROWTH M6_YOUTUBE_VIEWS_GROWTH M7_YOUTUBE_VIEWS_GROWTH M8_YOUTUBE_VIEWS_GROWTH M9_YOUTUBE_VIEWS_GROWTH M10_YOUTUBE_VIEWS_GROWTH M11_YOUTUBE_VIEWS_GROWTH M12_YOUTUBE_VIEWS_GROWTH M1_YOUTUBE_LIKES_GROWTH M2_YOUTUBE_LIKES_GROWTH M3_YOUTUBE_LIKES_GROWTH M4_YOUTUBE_LIKES_GROWTH M5_YOUTUBE_LIKES_GROWTH M6_YOUTUBE_LIKES_GROWTH M7_YOUTUBE_LIKES_GROWTH M8_YOUTUBE_LIKES_GROWTH M9_YOUTUBE_LIKES_GROWTH M10_YOUTUBE_LIKES_GROWTH M11_YOUTUBE_LIKES_GROWTH M12_YOUTUBE_LIKES_GROWTH M1_SPOTIFY_PLAYS_GROWTH M2_SPOTIFY_PLAYS_GROWTH M3_SPOTIFY_PLAYS_GROWTH M4_SPOTIFY_PLAYS_GROWTH M5_SPOTIFY_PLAYS_GROWTH M6_SPOTIFY_PLAYS_GROWTH M7_SPOTIFY_PLAYS_GROWTH M8_SPOTIFY_PLAYS_GROWTH M9_SPOTIFY_PLAYS_GROWTH M10_SPOTIFY_PLAYS_GROWTH M11_SPOTIFY_PLAYS_GROWTH M12_SPOTIFY_PLAYS_GROWTH M1_SPOTIFY_POPULARITY_GROWTH M2_SPOTIFY_POPULARITY_GROWTH M3_SPOTIFY_POPULARITY_GROWTH M4_SPOTIFY_POPULARITY_GROWTH M5_SPOTIFY_POPULARITY_GROWTH M6_SPOTIFY_POPULARITY_GROWTH M7_SPOTIFY_POPULARITY_GROWTH M8_SPOTIFY_POPULARITY_GROWTH M9_SPOTIFY_POPULARITY_GROWTH M10_SPOTIFY_POPULARITY_GROWTH M11_SPOTIFY_POPULARITY_GROWTH M12_SPOTIFY_POPULARITY_GROWTH M1_TIKTOK_POSTS_GROWTH M2_TIKTOK_POSTS_GROWTH M3_TIKTOK_POSTS_GROWTH M4_TIKTOK_POSTS_GROWTH M5_TIKTOK_POSTS_GROWTH M6_TIKTOK_POSTS_GROWTH M7_TIKTOK_POSTS_GROWTH M8_TIKTOK_POSTS_GROWTH M9_TIKTOK_POSTS_GROWTH M10_TIKTOK_POSTS_GROWTH M11_TIKTOK_POSTS_GROWTH M12_TIKTOK_POSTS_GROWTH M1_AIRPLAY_STREAMS_GROWTH M2_AIRPLAY_STREAMS_GROWTH M3_AIRPLAY_STREAMS_GROWTH M4_AIRPLAY_STREAMS_GROWTH M5_AIRPLAY_STREAMS_GROWTH M6_AIRPLAY_STREAMS_GROWTH M7_AIRPLAY_STREAMS_GROWTH M8_AIRPLAY_STREAMS_GROWTH M9_AIRPLAY_STREAMS_GROWTH M10_AIRPLAY_STREAMS_GROWTH M11_AIRPLAY_STREAMS_GROWTH M12_AIRPLAY_STREAMS_GROWTH WK1_YT_VIEWS_GROWTH WK2_YT_VIEWS_GROWTH WK3_YT_VIEWS_GROWTH WK4_YT_VIEWS_GROWTH WK5_YT_VIEWS_GROWTH WK6_YT_VIEWS_GROWTH WK7_YT_VIEWS_GROWTH WK8_YT_VIEWS_GROWTH WK9_YT_VIEWS_GROWTH WK10_YT_VIEWS_GROWTH WK11_YT_VIEWS_GROWTH WK12_YT_VIEWS_GROWTH WK13_YT_VIEWS_GROWTH WK14_YT_VIEWS_GROWTH WK15_YT_VIEWS_GROWTH WK1_SPOTIFY_PLAYS_GROWTH WK2_SPOTIFY_PLAYS_GROWTH WK3_SPOTIFY_PLAYS_GROWTH WK4_SPOTIFY_PLAYS_GROWTH WK5_SPOTIFY_PLAYS_GROWTH WK6_SPOTIFY_PLAYS_GROWTH WK7_SPOTIFY_PLAYS_GROWTH WK8_SPOTIFY_PLAYS_GROWTH WK9_SPOTIFY_PLAYS_GROWTH WK10_SPOTIFY_PLAYS_GROWTH WK11_SPOTIFY_PLAYS_GROWTH WK12_SPOTIFY_PLAYS_GROWTH WK13_SPOTIFY_PLAYS_GROWTH WK14_SPOTIFY_PLAYS_GROWTH WK15_SPOTIFY_PLAYS_GROWTH WK1_TIKTOK_POSTS_GROWTH WK2_TIKTOK_POSTS_GROWTH WK3_TIKTOK_POSTS_GROWTH WK4_TIKTOK_POSTS_GROWTH WK5_TIKTOK_POSTS_GROWTH WK6_TIKTOK_POSTS_GROWTH WK7_TIKTOK_POSTS_GROWTH WK8_TIKTOK_POSTS_GROWTH WK9_TIKTOK_POSTS_GROWTH WK10_TIKTOK_POSTS_GROWTH WK11_TIKTOK_POSTS_GROWTH WK12_TIKTOK_POSTS_GROWTH WK13_TIKTOK_POSTS_GROWTH WK14_TIKTOK_POSTS_GROWTH WK15_TIKTOK_POSTS_GROWTH
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1637 12760478 Walk of Life 3320514 Sultans Of Swing - The Very Best Of Dire Strai... 1998-11-10 Dire Straits GB United Kingdom 462941.0 Hard Rock 1 190232534.0 47301.0 0.0 37.0 121.0 4754.0 9050473.0 44976.0 0.0 9.0 49.0 4968.0 8877656.0 48117.0 23219669.0 -1.0 -447.0 5008.0 9129411.0 50740.0 22342960.0 2.0 616.0 4755.0 Hard Rock 0.0 3135347.0 3411246.0 3090998.0 3156404.0 2803071.0 3015935.0 3065314.0 2796407.0 2931368.0 2859852.0 3338191.0 0.0 15368.0 16634.0 14775.0 15715.0 14486.0 16457.0 16569.0 15091.0 16487.0 16006.0 18247.0 0.0 0.0 0.0 0.0 0.0 7363529.0 7716476.0 8590867.0 6912326.0 7726198.0 7430251.0 7186511.0 0.0 0.0 2.0 0.0 7.0 2.0 0.0 0.0 0.0 2.0 0.0 1.0 0.0 67.0 32.0 25.0 15.0 9.0 0.0 390.0 0.0 515.0 39.0 62.0 0.0 1487.0 1637.0 1653.0 1692.0 1623.0 1707.0 1675.0 1626.0 1611.0 1504.0 1640.0 0.0 0.0 0.0 183103054.0 0.0 0.0 184715830.0 186223982.0 0.0 0.0 188884632.0 0.0 0.0 0.0 189235476.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -284.0 0.0 0.0 0.0 0.0 373.0 0.0 -1126.0 0.0 -8637.0 0.0 548.0
1915 49664963 Vibe 11676166 Vibe 2021-06-18 Mike Candys CH Switzerland 462885.0 Dance 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 67.0 0.0 6.0 0.0 0.0 6609606.0 1.0 0.0 16.0 Dance 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2960144.0 2606502.0 2398479.0 2055240.0 2155887.0 0.0 0.0 0.0 0.0 0.0 0.0 64.0 3.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 14.0 0.0 0.0 27.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.0 0.0 0.0 6.0 9.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1945 28766256 Suspicious Minds 7942716 Suspicious Minds 2020-03-15 Flavio ES Spain 462958.0 Latin Hip-Hop/Rap 1 0.0 0.0 0.0 2.0 1.0 0.0 0.0 0.0 0.0 20.0 0.0 0.0 0.0 0.0 0.0 -5.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 Latin 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 18.0 0.0 3.0 0.0 15.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -212.0 8.0 0.0 0.0 -22084.0 0.0
2231 15682717 Islands In the Stream 1847470 Elokuvien 100 suosituinta 2010-02-08 Kenny Rogers US United States 462895.0 Country 1 16525310.0 81813.0 0.0 69.0 9.0 2548.0 4749886.0 23413.0 0.0 -1.0 -15.0 1866.0 4317447.0 23861.0 14031108.0 -2.0 -1149.0 1820.0 4325202.0 22869.0 13675769.0 2.0 1291.0 1477.0 Country 0.0 0.0 11292080.0 0.0 47682.0 15809923.0 0.0 18495497.0 1554832.0 1591491.0 1362253.0 1371458.0 0.0 0.0 34427.0 0.0 594.0 54849.0 0.0 68954.0 8747.0 8212.0 7290.0 7367.0 0.0 0.0 0.0 0.0 0.0 4483582.0 4752343.0 4547152.0 4731613.0 4700026.0 4581506.0 4394237.0 0.0 17.0 52.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 1.0 4.0 0.0 0.0 0.0 1.0 138.0 0.0 1430.0 0.0 0.0 767.0 897.0 872.0 452.0 542.0 578.0 579.0 663.0 617.0 504.0 356.0 0.0 0.0 0.0 0.0 -125658211.0 0.0 8396664.0 -649593937.0 0.0 0.0 0.0 0.0 15761423.0 0.0 4949215.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1334.0 0.0 1159.0 0.0 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
101911 59705222 Vibe 20597269 Haunted Haus 2022-04-08 Ghastly US United States 462890.0 House 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 413392.0 0.0 0.0 2.0 Dance 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 168078.0 132621.0 112693.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 4.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
101950 31948328 Forever 6999651 Forever 2020-11-13 rei brown US United States 462882.0 Pop 1 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 6.0 0.0 0.0 0.0 502808.0 0.0 -2.0 0.0 0.0 0.0 635995.0 0.0 10.0 0.0 Electronic 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 172341.0 169746.0 170566.0 162496.0 203819.0 202456.0 229720.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 2.0 1.0 3.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -10.0 0.0 0.0 0.0 0.0 11.0
101971 51828537 O Ex da Sua Vida 12128789 Falando de Amor, Vol. 2 2021-07-01 Gusttavo Lima BR Brazil 462951.0 Latin 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12996547.0 1.0 135.0 145.0 Latin 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7108849.0 5616118.0 4726408.0 3516757.0 4753382.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 212.0 90.0 58.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 16.0 0.0 17.0 72.0 56.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
102020 32013076 The Best Days (feat. Tabitha) 7065746 Home Sweet Home EP 2020-11-27 Sam Feldt NL Netherlands 462885.0 Dance 1 0.0 0.0 0.0 45.0 8.0 43.0 0.0 0.0 0.0 -2.0 39.0 25.0 0.0 0.0 9417866.0 -4.0 0.0 7.0 0.0 0.0 4777785.0 -1.0 0.0 8.0 Dance 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3622410.0 3382095.0 3607489.0 2428282.0 1817046.0 1555659.0 1405080.0 0.0 0.0 2.0 0.0 47.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.0 9.0 13.0 17.0 18.0 12.0 0.0 0.0 5.0 6.0 0.0 9.0 5.0 9.0 9.0 7.0 4.0 1.0 2.0 2.0 2.0 4.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 130.0 0.0 0.0 -11.0 105.0 -3616.0 0.0 115.0
102021 49090118 Pegador 11528373 Pegador 2021-06-12 JoĂŁo Bosco & Vinicius BR Brazil 462877.0 Sertanejo 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5526599.0 0.0 0.0 8.0 465271.0 2780.0 1587118.0 -7.0 0.0 0.0 Jazz 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 157237.0 143648.0 164386.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1015.0 858.0 907.0 0.0 0.0 0.0 0.0 0.0 0.0 2490112.0 1755999.0 1280488.0 779232.0 428865.0 379021.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

380 rows × 153 columns

In [105]:
negative_1_s.ARTIST_GENRE.value_counts()
Out[105]:
Pop                   83
Rock                  49
R&B/Soul              29
Hip-Hop/Rap           27
Country               21
Electronic            20
Dance                 16
Alternative           16
Latin                 15
Metal                  9
House                  8
Latin Pop              8
Classical              7
Jazz                   7
Vocal                  6
Hard Rock              6
Holiday                4
Soft Rock              4
Christian              4
Indie Rock             3
Blues                  3
Latin Rock             2
Reggae                 2
Punk                   2
Latin Hip-Hop/Rap      2
Singer/Songwriter      2
Easy Listening         2
Children's Music       2
Musicals               2
Others                 1
Forro                  1
African                1
Schlager               1
J-Pop                  1
Grunge                 1
Indian Hip-Hop/Rap     1
Techno                 1
K-Pop                  1
New Wave               1
Swing                  1
New Age                1
Classic Rock           1
Salsa                  1
Sertanejo              1
Name: ARTIST_GENRE, dtype: int64

spotify cluster -1 Country¶

In [113]:
df_spotify_cn1_country = negative_1_s.COUNTRY_NAME.value_counts()
df_spotify_cn1_country = pd.DataFrame(data=df_spotify_cn1_country)
df_spotify_cn1_country.columns = ['count']
df_spotify_cn1_country.plot.bar()
Out[113]:
<AxesSubplot:>

spotify cluster -1 Genre¶

In [112]:
df_spotify_cn1_genre = negative_1_s.ARTIST_GENRE.value_counts()
df_spotify_cn1_genre = pd.DataFrame(data=df_youtube_c1_genre)
df_spotify_cn1_genre.columns = ['count']
df_spotify_cn1_genre.plot.bar()
Out[112]:
<AxesSubplot:>
In [111]:
### Investigate cluster 0

df_0 = df_no_zero_spotify.query('cluster == 0')
list_0 = df_0.index.tolist()

zero_s = df.query("TRACK_NAME in @list_0")
zero_s
Out[111]:
CM_TRACK TRACK_NAME CM_ALBUM ALBUM_NAME RELEASE_DATE ARTIST_NAME ARTIST_COUNTRY_CODE2 COUNTRY_NAME GENRE_ID ARTIST_GENRE DUPE Q1_YOUTUBE_VIEWS_GROWTH Q1_YOUTUBE_LIKES_GROWTH Q1_SPOTIFY_PLAYS_GROWTH Q1_SPOTIFY_POPULARITY_GROWTH Q1_TIKTOK_POSTS_GROWTH Q1_AIRPLAY_STREAMS_GROWTH Q2_YOUTUBE_VIEWS_GROWTH Q2_YOUTUBE_LIKES_GROWTH Q2_SPOTIFY_PLAYS_GROWTH Q2_SPOTIFY_POPULARITY_GROWTH Q2_TIKTOK_POSTS_GROWTH Q2_AIRPLAY_STREAMS_GROWTH Q3_YOUTUBE_VIEWS_GROWTH Q3_YOUTUBE_LIKES_GROWTH Q3_SPOTIFY_PLAYS_GROWTH Q3_SPOTIFY_POPULARITY_GROWTH Q3_TIKTOK_POSTS_GROWTH Q3_AIRPLAY_STREAMS_GROWTH Q4_YOUTUBE_VIEWS_GROWTH Q4_YOUTUBE_LIKES_GROWTH Q4_SPOTIFY_PLAYS_GROWTH Q4_SPOTIFY_POPULARITY_GROWTH Q4_TIKTOK_POSTS_GROWTH Q4_AIRPLAY_STREAMS_GROWTH GENRE M1_YOUTUBE_VIEWS_GROWTH M2_YOUTUBE_VIEWS_GROWTH M3_YOUTUBE_VIEWS_GROWTH M4_YOUTUBE_VIEWS_GROWTH M5_YOUTUBE_VIEWS_GROWTH M6_YOUTUBE_VIEWS_GROWTH M7_YOUTUBE_VIEWS_GROWTH M8_YOUTUBE_VIEWS_GROWTH M9_YOUTUBE_VIEWS_GROWTH M10_YOUTUBE_VIEWS_GROWTH M11_YOUTUBE_VIEWS_GROWTH M12_YOUTUBE_VIEWS_GROWTH M1_YOUTUBE_LIKES_GROWTH M2_YOUTUBE_LIKES_GROWTH M3_YOUTUBE_LIKES_GROWTH M4_YOUTUBE_LIKES_GROWTH M5_YOUTUBE_LIKES_GROWTH M6_YOUTUBE_LIKES_GROWTH M7_YOUTUBE_LIKES_GROWTH M8_YOUTUBE_LIKES_GROWTH M9_YOUTUBE_LIKES_GROWTH M10_YOUTUBE_LIKES_GROWTH M11_YOUTUBE_LIKES_GROWTH M12_YOUTUBE_LIKES_GROWTH M1_SPOTIFY_PLAYS_GROWTH M2_SPOTIFY_PLAYS_GROWTH M3_SPOTIFY_PLAYS_GROWTH M4_SPOTIFY_PLAYS_GROWTH M5_SPOTIFY_PLAYS_GROWTH M6_SPOTIFY_PLAYS_GROWTH M7_SPOTIFY_PLAYS_GROWTH M8_SPOTIFY_PLAYS_GROWTH M9_SPOTIFY_PLAYS_GROWTH M10_SPOTIFY_PLAYS_GROWTH M11_SPOTIFY_PLAYS_GROWTH M12_SPOTIFY_PLAYS_GROWTH M1_SPOTIFY_POPULARITY_GROWTH M2_SPOTIFY_POPULARITY_GROWTH M3_SPOTIFY_POPULARITY_GROWTH M4_SPOTIFY_POPULARITY_GROWTH M5_SPOTIFY_POPULARITY_GROWTH M6_SPOTIFY_POPULARITY_GROWTH M7_SPOTIFY_POPULARITY_GROWTH M8_SPOTIFY_POPULARITY_GROWTH M9_SPOTIFY_POPULARITY_GROWTH M10_SPOTIFY_POPULARITY_GROWTH M11_SPOTIFY_POPULARITY_GROWTH M12_SPOTIFY_POPULARITY_GROWTH M1_TIKTOK_POSTS_GROWTH M2_TIKTOK_POSTS_GROWTH M3_TIKTOK_POSTS_GROWTH M4_TIKTOK_POSTS_GROWTH M5_TIKTOK_POSTS_GROWTH M6_TIKTOK_POSTS_GROWTH M7_TIKTOK_POSTS_GROWTH M8_TIKTOK_POSTS_GROWTH M9_TIKTOK_POSTS_GROWTH M10_TIKTOK_POSTS_GROWTH M11_TIKTOK_POSTS_GROWTH M12_TIKTOK_POSTS_GROWTH M1_AIRPLAY_STREAMS_GROWTH M2_AIRPLAY_STREAMS_GROWTH M3_AIRPLAY_STREAMS_GROWTH M4_AIRPLAY_STREAMS_GROWTH M5_AIRPLAY_STREAMS_GROWTH M6_AIRPLAY_STREAMS_GROWTH M7_AIRPLAY_STREAMS_GROWTH M8_AIRPLAY_STREAMS_GROWTH M9_AIRPLAY_STREAMS_GROWTH M10_AIRPLAY_STREAMS_GROWTH M11_AIRPLAY_STREAMS_GROWTH M12_AIRPLAY_STREAMS_GROWTH WK1_YT_VIEWS_GROWTH WK2_YT_VIEWS_GROWTH WK3_YT_VIEWS_GROWTH WK4_YT_VIEWS_GROWTH WK5_YT_VIEWS_GROWTH WK6_YT_VIEWS_GROWTH WK7_YT_VIEWS_GROWTH WK8_YT_VIEWS_GROWTH WK9_YT_VIEWS_GROWTH WK10_YT_VIEWS_GROWTH WK11_YT_VIEWS_GROWTH WK12_YT_VIEWS_GROWTH WK13_YT_VIEWS_GROWTH WK14_YT_VIEWS_GROWTH WK15_YT_VIEWS_GROWTH WK1_SPOTIFY_PLAYS_GROWTH WK2_SPOTIFY_PLAYS_GROWTH WK3_SPOTIFY_PLAYS_GROWTH WK4_SPOTIFY_PLAYS_GROWTH WK5_SPOTIFY_PLAYS_GROWTH WK6_SPOTIFY_PLAYS_GROWTH WK7_SPOTIFY_PLAYS_GROWTH WK8_SPOTIFY_PLAYS_GROWTH WK9_SPOTIFY_PLAYS_GROWTH WK10_SPOTIFY_PLAYS_GROWTH WK11_SPOTIFY_PLAYS_GROWTH WK12_SPOTIFY_PLAYS_GROWTH WK13_SPOTIFY_PLAYS_GROWTH WK14_SPOTIFY_PLAYS_GROWTH WK15_SPOTIFY_PLAYS_GROWTH WK1_TIKTOK_POSTS_GROWTH WK2_TIKTOK_POSTS_GROWTH WK3_TIKTOK_POSTS_GROWTH WK4_TIKTOK_POSTS_GROWTH WK5_TIKTOK_POSTS_GROWTH WK6_TIKTOK_POSTS_GROWTH WK7_TIKTOK_POSTS_GROWTH WK8_TIKTOK_POSTS_GROWTH WK9_TIKTOK_POSTS_GROWTH WK10_TIKTOK_POSTS_GROWTH WK11_TIKTOK_POSTS_GROWTH WK12_TIKTOK_POSTS_GROWTH WK13_TIKTOK_POSTS_GROWTH WK14_TIKTOK_POSTS_GROWTH WK15_TIKTOK_POSTS_GROWTH
6 33741047 Over 12527653 Afterhour 2021-05-14 FOURTY DE Germany 462883.0 Hip-Hop/Rap 1 0.0 0.0 0.0 30.0 0.0 12.0 0.0 0.0 0.0 -9.0 0.0 81.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 Dance 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 75.0 3.0 3.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -845.0
15 11657825 Nocturne No.1 In B Flat Minor, Op.9 No.1 12375744 Classical For Gardening 2021-07-09 Frédéric Chopin PL Poland 462894.0 New Age 1 0.0 0.0 0.0 60.0 0.0 9.0 0.0 0.0 0.0 -1.0 0.0 12.0 0.0 0.0 2598859.0 2.0 0.0 4.0 0.0 0.0 3278214.0 3.0 0.0 8.0 Classical 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 605496.0 730399.0 855677.0 1012783.0 1139428.0 1099183.0 1039603.0 0.0 1.0 1.0 0.0 0.0 40.0 0.0 0.0 42.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 5.0 4.0 3.0 2.0 2.0 0.0 1.0 5.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
23 32875570 Lonely (feat. Malou) 12555699 Lonely (feat. Malou) - Single 2021-02-19 Malou DE Germany 462885.0 Dance 1 0.0 0.0 0.0 53.0 3.0 21.0 0.0 0.0 0.0 -3.0 2.0 29.0 0.0 0.0 380828.0 0.0 2.0 16.0 0.0 0.0 194705.0 0.0 1.0 18.0 Dance 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 172136.0 130752.0 132138.0 117938.0 90096.0 56026.0 48583.0 0.0 0.0 53.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8.0 11.0 13.0 5.0 1.0 2.0 13.0 13.0 3.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -84.0 -322.0 0.0 13.0
27 15966354 Flex 147413 Flex (feat. Tory Lanez & Fabolous) - Single 2016-07-01 Joe Budden US United States 462883.0 Hip-Hop/Rap 1 1260510.0 88.0 0.0 2.0 0.0 0.0 7031.0 76.0 0.0 -1.0 0.0 0.0 6931.0 75.0 0.0 -3.0 0.0 0.0 5891.0 68.0 0.0 2.0 0.0 0.0 Hip-Hop/Rap 0.0 1848.0 2577.0 2534.0 2138.0 2359.0 2288.0 2168.0 2475.0 1527.0 2125.0 2239.0 0.0 38.0 25.0 23.0 27.0 26.0 22.0 25.0 28.0 21.0 22.0 25.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -9994086.0 0.0 -35213594.0 0.0 0.0 0.0 -7608523.0 -5207633.0 -5500156.0 949964.0 -344181195.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
32 20563402 To The End 6842991 Fall For Jesus 2020-10-09 Mack Brock US United States 462879.0 Christian 1 0.0 0.0 0.0 48.0 0.0 0.0 0.0 0.0 0.0 -2.0 0.0 0.0 0.0 0.0 275306.0 -46.0 0.0 0.0 0.0 0.0 333607.0 0.0 0.0 0.0 Christian 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 86467.0 90133.0 96965.0 88208.0 102820.0 112978.0 117809.0 0.0 0.0 42.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
102512 31721983 Underdog 6898771 Underdog 2020-10-19 Ummet Ozcan NL Netherlands 462890.0 House 1 0.0 0.0 0.0 0.0 0.0 21.0 0.0 0.0 0.0 -3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Electronic 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
102520 47079324 Harraga 11032308 Harraga 2021-06-03 YONII AT Austria 462883.0 Hip-Hop/Rap 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 358284.0 0.0 0.0 0.0 0.0 0.0 197077.0 0.0 2.0 0.0 Hip-Hop/Rap 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 167618.0 110978.0 79688.0 70854.0 57052.0 69171.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
102529 18618298 Over the Rainbow 961194 Christmas Hits 2007 2007-11-26 Eva Cassidy US United States 462893.0 Jazz 1 0.0 0.0 0.0 2.0 183.0 0.0 0.0 0.0 0.0 -1.0 36.0 0.0 0.0 0.0 963252.0 -52.0 -233.0 0.0 0.0 0.0 1032416.0 54.0 244.0 0.0 Folk 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 273709.0 300647.0 345797.0 316808.0 356964.0 348952.0 326500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 53.0 0.0 1.0 0.0 148.0 20.0 24.0 3.0 9.0 0.0 241.0 0.0 3.0 37.0 204.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -15278.0 0.0 0.0 106.0 0.0 0.0 -421.0 -27908.0 195.0 0.0 203.0 -216.0 0.0
102530 33435556 44BABY 7728896 44BABY 2021-03-11 NARU DE Germany 462883.0 Hip-Hop/Rap 1 0.0 0.0 0.0 59.0 2.0 0.0 0.0 0.0 0.0 -10.0 -1.0 0.0 0.0 0.0 206363.0 -41.0 0.0 0.0 0.0 0.0 115913.0 3.0 0.0 0.0 Hip-Hop/Rap 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 121935.0 90967.0 63754.0 51642.0 43803.0 32884.0 39226.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -189.0 0.0 -435.0
102544 17119125 I'll Find My Way Home - Remastered 6939072 Christmas Chill 2020-10-30 Jon & Vangelis GR Greece 462882.0 Pop 1 0.0 0.0 0.0 28.0 0.0 0.0 0.0 0.0 0.0 0.0 7.0 37.0 0.0 0.0 755767.0 -34.0 -7.0 377.0 0.0 0.0 805288.0 10.0 25.0 377.0 New Age 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 264414.0 260644.0 257199.0 237924.0 261847.0 213170.0 330271.0 0.0 0.0 3.0 0.0 0.0 1.0 0.0 2.0 0.0 9.0 0.0 12.0 0.0 0.0 0.0 3.0 3.0 1.0 6.0 0.0 0.0 0.0 0.0 25.0 0.0 0.0 0.0 0.0 0.0 37.0 117.0 142.0 118.0 146.0 115.0 116.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -245.0 0.0 0.0 -18.0 -254.0

8426 rows × 153 columns

Spotify cluster 0 Country¶

In [116]:
df_spotify_c0 = zero_s.COUNTRY_NAME.value_counts()
df_spotify_c0 = pd.DataFrame(data=df_spotify_c0)
df_spotify_c0.columns = ['count']
df_spotify_c0.plot.bar()
Out[116]:
<AxesSubplot:>

Spotify cluster 0 Genre¶

In [115]:
df_spotify_c0_genre = zero_s.ARTIST_GENRE.value_counts()
df_spotify_c0_genre = pd.DataFrame(data=df_spotify_c0_genre)
df_spotify_c0_genre.columns = ['count']
df_spotify_c0_genre.plot.bar()
Out[115]:
<AxesSubplot:>
In [119]:
### Investigate cluster 1

df_1_s = df_no_zero_spotify.query('cluster == 1')
list_1_s = df_1_s.index.tolist()

one_s = df.query("TRACK_NAME in @list_1_s")
one_s.head()
Out[119]:
CM_TRACK TRACK_NAME CM_ALBUM ALBUM_NAME RELEASE_DATE ARTIST_NAME ARTIST_COUNTRY_CODE2 COUNTRY_NAME GENRE_ID ARTIST_GENRE DUPE Q1_YOUTUBE_VIEWS_GROWTH Q1_YOUTUBE_LIKES_GROWTH Q1_SPOTIFY_PLAYS_GROWTH Q1_SPOTIFY_POPULARITY_GROWTH Q1_TIKTOK_POSTS_GROWTH Q1_AIRPLAY_STREAMS_GROWTH Q2_YOUTUBE_VIEWS_GROWTH Q2_YOUTUBE_LIKES_GROWTH Q2_SPOTIFY_PLAYS_GROWTH Q2_SPOTIFY_POPULARITY_GROWTH Q2_TIKTOK_POSTS_GROWTH Q2_AIRPLAY_STREAMS_GROWTH Q3_YOUTUBE_VIEWS_GROWTH Q3_YOUTUBE_LIKES_GROWTH Q3_SPOTIFY_PLAYS_GROWTH Q3_SPOTIFY_POPULARITY_GROWTH Q3_TIKTOK_POSTS_GROWTH Q3_AIRPLAY_STREAMS_GROWTH Q4_YOUTUBE_VIEWS_GROWTH Q4_YOUTUBE_LIKES_GROWTH Q4_SPOTIFY_PLAYS_GROWTH Q4_SPOTIFY_POPULARITY_GROWTH Q4_TIKTOK_POSTS_GROWTH Q4_AIRPLAY_STREAMS_GROWTH GENRE M1_YOUTUBE_VIEWS_GROWTH M2_YOUTUBE_VIEWS_GROWTH M3_YOUTUBE_VIEWS_GROWTH M4_YOUTUBE_VIEWS_GROWTH M5_YOUTUBE_VIEWS_GROWTH M6_YOUTUBE_VIEWS_GROWTH M7_YOUTUBE_VIEWS_GROWTH M8_YOUTUBE_VIEWS_GROWTH M9_YOUTUBE_VIEWS_GROWTH M10_YOUTUBE_VIEWS_GROWTH M11_YOUTUBE_VIEWS_GROWTH M12_YOUTUBE_VIEWS_GROWTH M1_YOUTUBE_LIKES_GROWTH M2_YOUTUBE_LIKES_GROWTH M3_YOUTUBE_LIKES_GROWTH M4_YOUTUBE_LIKES_GROWTH M5_YOUTUBE_LIKES_GROWTH M6_YOUTUBE_LIKES_GROWTH M7_YOUTUBE_LIKES_GROWTH M8_YOUTUBE_LIKES_GROWTH M9_YOUTUBE_LIKES_GROWTH M10_YOUTUBE_LIKES_GROWTH M11_YOUTUBE_LIKES_GROWTH M12_YOUTUBE_LIKES_GROWTH M1_SPOTIFY_PLAYS_GROWTH M2_SPOTIFY_PLAYS_GROWTH M3_SPOTIFY_PLAYS_GROWTH M4_SPOTIFY_PLAYS_GROWTH M5_SPOTIFY_PLAYS_GROWTH M6_SPOTIFY_PLAYS_GROWTH M7_SPOTIFY_PLAYS_GROWTH M8_SPOTIFY_PLAYS_GROWTH M9_SPOTIFY_PLAYS_GROWTH M10_SPOTIFY_PLAYS_GROWTH M11_SPOTIFY_PLAYS_GROWTH M12_SPOTIFY_PLAYS_GROWTH M1_SPOTIFY_POPULARITY_GROWTH M2_SPOTIFY_POPULARITY_GROWTH M3_SPOTIFY_POPULARITY_GROWTH M4_SPOTIFY_POPULARITY_GROWTH M5_SPOTIFY_POPULARITY_GROWTH M6_SPOTIFY_POPULARITY_GROWTH M7_SPOTIFY_POPULARITY_GROWTH M8_SPOTIFY_POPULARITY_GROWTH M9_SPOTIFY_POPULARITY_GROWTH M10_SPOTIFY_POPULARITY_GROWTH M11_SPOTIFY_POPULARITY_GROWTH M12_SPOTIFY_POPULARITY_GROWTH M1_TIKTOK_POSTS_GROWTH M2_TIKTOK_POSTS_GROWTH M3_TIKTOK_POSTS_GROWTH M4_TIKTOK_POSTS_GROWTH M5_TIKTOK_POSTS_GROWTH M6_TIKTOK_POSTS_GROWTH M7_TIKTOK_POSTS_GROWTH M8_TIKTOK_POSTS_GROWTH M9_TIKTOK_POSTS_GROWTH M10_TIKTOK_POSTS_GROWTH M11_TIKTOK_POSTS_GROWTH M12_TIKTOK_POSTS_GROWTH M1_AIRPLAY_STREAMS_GROWTH M2_AIRPLAY_STREAMS_GROWTH M3_AIRPLAY_STREAMS_GROWTH M4_AIRPLAY_STREAMS_GROWTH M5_AIRPLAY_STREAMS_GROWTH M6_AIRPLAY_STREAMS_GROWTH M7_AIRPLAY_STREAMS_GROWTH M8_AIRPLAY_STREAMS_GROWTH M9_AIRPLAY_STREAMS_GROWTH M10_AIRPLAY_STREAMS_GROWTH M11_AIRPLAY_STREAMS_GROWTH M12_AIRPLAY_STREAMS_GROWTH WK1_YT_VIEWS_GROWTH WK2_YT_VIEWS_GROWTH WK3_YT_VIEWS_GROWTH WK4_YT_VIEWS_GROWTH WK5_YT_VIEWS_GROWTH WK6_YT_VIEWS_GROWTH WK7_YT_VIEWS_GROWTH WK8_YT_VIEWS_GROWTH WK9_YT_VIEWS_GROWTH WK10_YT_VIEWS_GROWTH WK11_YT_VIEWS_GROWTH WK12_YT_VIEWS_GROWTH WK13_YT_VIEWS_GROWTH WK14_YT_VIEWS_GROWTH WK15_YT_VIEWS_GROWTH WK1_SPOTIFY_PLAYS_GROWTH WK2_SPOTIFY_PLAYS_GROWTH WK3_SPOTIFY_PLAYS_GROWTH WK4_SPOTIFY_PLAYS_GROWTH WK5_SPOTIFY_PLAYS_GROWTH WK6_SPOTIFY_PLAYS_GROWTH WK7_SPOTIFY_PLAYS_GROWTH WK8_SPOTIFY_PLAYS_GROWTH WK9_SPOTIFY_PLAYS_GROWTH WK10_SPOTIFY_PLAYS_GROWTH WK11_SPOTIFY_PLAYS_GROWTH WK12_SPOTIFY_PLAYS_GROWTH WK13_SPOTIFY_PLAYS_GROWTH WK14_SPOTIFY_PLAYS_GROWTH WK15_SPOTIFY_PLAYS_GROWTH WK1_TIKTOK_POSTS_GROWTH WK2_TIKTOK_POSTS_GROWTH WK3_TIKTOK_POSTS_GROWTH WK4_TIKTOK_POSTS_GROWTH WK5_TIKTOK_POSTS_GROWTH WK6_TIKTOK_POSTS_GROWTH WK7_TIKTOK_POSTS_GROWTH WK8_TIKTOK_POSTS_GROWTH WK9_TIKTOK_POSTS_GROWTH WK10_TIKTOK_POSTS_GROWTH WK11_TIKTOK_POSTS_GROWTH WK12_TIKTOK_POSTS_GROWTH WK13_TIKTOK_POSTS_GROWTH WK14_TIKTOK_POSTS_GROWTH WK15_TIKTOK_POSTS_GROWTH
523 15117472 Me And My B*tch 3964024 Ready To Die The Remaster 1994-09-13 The Notorious B.I.G. US United States 462883.0 Hip-Hop/Rap 1 34972.0 27.0 1084365.0 25.0 38.0 6.0 0.0 0.0 0.0 -4.0 28.0 5.0 0.0 0.0 0.0 -53.0 -162.0 0.0 2579.0 55.0 796096.0 53.0 199.0 0.0 Hip-Hop/Rap 0.0 0.0 1859.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 623.0 0.0 0.0 27.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12.0 0.0 273562.0 544084.0 346690.0 316041.0 0.0 0.0 300789.0 246539.0 273199.0 261627.0 261270.0 0.0 1.0 5.0 0.0 0.0 19.0 0.0 51.0 0.0 53.0 0.0 0.0 0.0 17.0 18.0 8.0 6.0 14.0 0.0 84.0 0.0 198.0 1.0 0.0 0.0 0.0 6.0 0.0 3.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 33505.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1378.0 106.0 0.0 0.0 127.0 0.0 0.0 0.0 101.0
965 22381921 Mother 7747173 Novidades Rock 2021-03-12 The Amazons GB United Kingdom 462884.0 Rock 1 0.0 0.0 0.0 42.0 0.0 26.0 0.0 0.0 0.0 2.0 0.0 28.0 0.0 0.0 0.0 -44.0 0.0 44.0 0.0 0.0 0.0 1.0 0.0 30.0 Rock 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 42.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.0 13.0 16.0 3.0 9.0 15.0 16.0 13.0 8.0 12.0 10.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3452 13069086 Glimmer 3082170 Given To The Wild 2012-04-24 The Maccabees GB United Kingdom 462888.0 Alternative 1 0.0 0.0 0.0 26.0 0.0 0.0 0.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 1.0 Alternative 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 28.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
12192 15895897 Light It Up 3254120 Black 2016-06-10 Dierks Bentley US United States 462895.0 Country 1 387204.0 0.0 0.0 37.0 0.0 0.0 3003.0 21.0 0.0 1.0 0.0 0.0 4000.0 21.0 0.0 0.0 0.0 0.0 3074.0 16.0 0.0 0.0 0.0 0.0 Country 0.0 0.0 0.0 918.0 977.0 1108.0 1723.0 1184.0 1093.0 880.0 1052.0 1142.0 0.0 0.0 0.0 6.0 3.0 12.0 8.0 2.0 11.0 6.0 6.0 4.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -187634654.0 -243830.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -493.0 0.0 -4527.0 -25000.0
17004 17722240 Mother 2156928 Christmas Songs 2017-11-10 Love & The Outcome US United States 462879.0 Christian 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 18.0 0.0 0.0 Christian 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 21.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Spotify cluster 1 Country¶

In [120]:
df_spotify_c1 = one_s.COUNTRY_NAME.value_counts()
df_spotify_c1 = pd.DataFrame(data=df_spotify_c1)
df_spotify_c1.columns = ['count']
df_spotify_c1.plot.bar()
Out[120]:
<AxesSubplot:>

Spotify cluster 1 Genre¶

In [121]:
df_spotify_c1_genre = one_s.ARTIST_GENRE.value_counts()
df_spotify_c1_genre = pd.DataFrame(data=df_spotify_c1_genre)
df_spotify_c1_genre.columns = ['count']
df_spotify_c1_genre.plot.bar()
Out[121]:
<AxesSubplot:>
In [122]:
### Investigate cluster 2

df_2_s = df_no_zero_spotify.query('cluster == 2')
list_2_s = df_2_s.index.tolist()

two_s = df.query("TRACK_NAME in @list_2_s")
two_s.head()
Out[122]:
CM_TRACK TRACK_NAME CM_ALBUM ALBUM_NAME RELEASE_DATE ARTIST_NAME ARTIST_COUNTRY_CODE2 COUNTRY_NAME GENRE_ID ARTIST_GENRE DUPE Q1_YOUTUBE_VIEWS_GROWTH Q1_YOUTUBE_LIKES_GROWTH Q1_SPOTIFY_PLAYS_GROWTH Q1_SPOTIFY_POPULARITY_GROWTH Q1_TIKTOK_POSTS_GROWTH Q1_AIRPLAY_STREAMS_GROWTH Q2_YOUTUBE_VIEWS_GROWTH Q2_YOUTUBE_LIKES_GROWTH Q2_SPOTIFY_PLAYS_GROWTH Q2_SPOTIFY_POPULARITY_GROWTH Q2_TIKTOK_POSTS_GROWTH Q2_AIRPLAY_STREAMS_GROWTH Q3_YOUTUBE_VIEWS_GROWTH Q3_YOUTUBE_LIKES_GROWTH Q3_SPOTIFY_PLAYS_GROWTH Q3_SPOTIFY_POPULARITY_GROWTH Q3_TIKTOK_POSTS_GROWTH Q3_AIRPLAY_STREAMS_GROWTH Q4_YOUTUBE_VIEWS_GROWTH Q4_YOUTUBE_LIKES_GROWTH Q4_SPOTIFY_PLAYS_GROWTH Q4_SPOTIFY_POPULARITY_GROWTH Q4_TIKTOK_POSTS_GROWTH Q4_AIRPLAY_STREAMS_GROWTH GENRE M1_YOUTUBE_VIEWS_GROWTH M2_YOUTUBE_VIEWS_GROWTH M3_YOUTUBE_VIEWS_GROWTH M4_YOUTUBE_VIEWS_GROWTH M5_YOUTUBE_VIEWS_GROWTH M6_YOUTUBE_VIEWS_GROWTH M7_YOUTUBE_VIEWS_GROWTH M8_YOUTUBE_VIEWS_GROWTH M9_YOUTUBE_VIEWS_GROWTH M10_YOUTUBE_VIEWS_GROWTH M11_YOUTUBE_VIEWS_GROWTH M12_YOUTUBE_VIEWS_GROWTH M1_YOUTUBE_LIKES_GROWTH M2_YOUTUBE_LIKES_GROWTH M3_YOUTUBE_LIKES_GROWTH M4_YOUTUBE_LIKES_GROWTH M5_YOUTUBE_LIKES_GROWTH M6_YOUTUBE_LIKES_GROWTH M7_YOUTUBE_LIKES_GROWTH M8_YOUTUBE_LIKES_GROWTH M9_YOUTUBE_LIKES_GROWTH M10_YOUTUBE_LIKES_GROWTH M11_YOUTUBE_LIKES_GROWTH M12_YOUTUBE_LIKES_GROWTH M1_SPOTIFY_PLAYS_GROWTH M2_SPOTIFY_PLAYS_GROWTH M3_SPOTIFY_PLAYS_GROWTH M4_SPOTIFY_PLAYS_GROWTH M5_SPOTIFY_PLAYS_GROWTH M6_SPOTIFY_PLAYS_GROWTH M7_SPOTIFY_PLAYS_GROWTH M8_SPOTIFY_PLAYS_GROWTH M9_SPOTIFY_PLAYS_GROWTH M10_SPOTIFY_PLAYS_GROWTH M11_SPOTIFY_PLAYS_GROWTH M12_SPOTIFY_PLAYS_GROWTH M1_SPOTIFY_POPULARITY_GROWTH M2_SPOTIFY_POPULARITY_GROWTH M3_SPOTIFY_POPULARITY_GROWTH M4_SPOTIFY_POPULARITY_GROWTH M5_SPOTIFY_POPULARITY_GROWTH M6_SPOTIFY_POPULARITY_GROWTH M7_SPOTIFY_POPULARITY_GROWTH M8_SPOTIFY_POPULARITY_GROWTH M9_SPOTIFY_POPULARITY_GROWTH M10_SPOTIFY_POPULARITY_GROWTH M11_SPOTIFY_POPULARITY_GROWTH M12_SPOTIFY_POPULARITY_GROWTH M1_TIKTOK_POSTS_GROWTH M2_TIKTOK_POSTS_GROWTH M3_TIKTOK_POSTS_GROWTH M4_TIKTOK_POSTS_GROWTH M5_TIKTOK_POSTS_GROWTH M6_TIKTOK_POSTS_GROWTH M7_TIKTOK_POSTS_GROWTH M8_TIKTOK_POSTS_GROWTH M9_TIKTOK_POSTS_GROWTH M10_TIKTOK_POSTS_GROWTH M11_TIKTOK_POSTS_GROWTH M12_TIKTOK_POSTS_GROWTH M1_AIRPLAY_STREAMS_GROWTH M2_AIRPLAY_STREAMS_GROWTH M3_AIRPLAY_STREAMS_GROWTH M4_AIRPLAY_STREAMS_GROWTH M5_AIRPLAY_STREAMS_GROWTH M6_AIRPLAY_STREAMS_GROWTH M7_AIRPLAY_STREAMS_GROWTH M8_AIRPLAY_STREAMS_GROWTH M9_AIRPLAY_STREAMS_GROWTH M10_AIRPLAY_STREAMS_GROWTH M11_AIRPLAY_STREAMS_GROWTH M12_AIRPLAY_STREAMS_GROWTH WK1_YT_VIEWS_GROWTH WK2_YT_VIEWS_GROWTH WK3_YT_VIEWS_GROWTH WK4_YT_VIEWS_GROWTH WK5_YT_VIEWS_GROWTH WK6_YT_VIEWS_GROWTH WK7_YT_VIEWS_GROWTH WK8_YT_VIEWS_GROWTH WK9_YT_VIEWS_GROWTH WK10_YT_VIEWS_GROWTH WK11_YT_VIEWS_GROWTH WK12_YT_VIEWS_GROWTH WK13_YT_VIEWS_GROWTH WK14_YT_VIEWS_GROWTH WK15_YT_VIEWS_GROWTH WK1_SPOTIFY_PLAYS_GROWTH WK2_SPOTIFY_PLAYS_GROWTH WK3_SPOTIFY_PLAYS_GROWTH WK4_SPOTIFY_PLAYS_GROWTH WK5_SPOTIFY_PLAYS_GROWTH WK6_SPOTIFY_PLAYS_GROWTH WK7_SPOTIFY_PLAYS_GROWTH WK8_SPOTIFY_PLAYS_GROWTH WK9_SPOTIFY_PLAYS_GROWTH WK10_SPOTIFY_PLAYS_GROWTH WK11_SPOTIFY_PLAYS_GROWTH WK12_SPOTIFY_PLAYS_GROWTH WK13_SPOTIFY_PLAYS_GROWTH WK14_SPOTIFY_PLAYS_GROWTH WK15_SPOTIFY_PLAYS_GROWTH WK1_TIKTOK_POSTS_GROWTH WK2_TIKTOK_POSTS_GROWTH WK3_TIKTOK_POSTS_GROWTH WK4_TIKTOK_POSTS_GROWTH WK5_TIKTOK_POSTS_GROWTH WK6_TIKTOK_POSTS_GROWTH WK7_TIKTOK_POSTS_GROWTH WK8_TIKTOK_POSTS_GROWTH WK9_TIKTOK_POSTS_GROWTH WK10_TIKTOK_POSTS_GROWTH WK11_TIKTOK_POSTS_GROWTH WK12_TIKTOK_POSTS_GROWTH WK13_TIKTOK_POSTS_GROWTH WK14_TIKTOK_POSTS_GROWTH WK15_TIKTOK_POSTS_GROWTH
237 32328050 Eternal Flame 7187918 Eternal Flame 2020-12-18 PENTAGON KR South Korea 462882.0 Pop 1 0.0 0.0 0.0 50.0 0.0 49.0 0.0 0.0 0.0 -6.0 0.0 21.0 0.0 0.0 0.0 -44.0 9.0 31.0 0.0 0.0 0.0 0.0 0.0 33.0 K-Pop 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 5.0 0.0 0.0 0.0 0.0 0.0 0.0 8.0 6.0 5.0 9.0 7.0 12.0 9.0 10.0 12.0 8.0 13.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
509 21815691 Starlight 3365695 Starlight 2018-10-19 BABYMETAL JP Japan 462884.0 Rock 1 5179619.0 3264.0 0.0 22.0 47.0 4.0 156612.0 2625.0 0.0 24.0 20.0 0.0 153045.0 2502.0 0.0 -36.0 -62.0 5.0 129372.0 2046.0 0.0 16.0 73.0 4.0 Hard Rock 0.0 70830.0 75610.0 56308.0 56591.0 43713.0 51029.0 54787.0 47229.0 46026.0 42660.0 40686.0 0.0 1258.0 1409.0 909.0 978.0 738.0 901.0 868.0 733.0 751.0 654.0 641.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9.0 0.0 32.0 0.0 0.0 0.0 10.0 17.0 0.0 0.0 0.0 0.0 47.0 12.0 6.0 2.0 0.0 6.0 0.0 1.0 6.0 66.0 0.0 0.0 1.0 0.0 0.0 0.0 2.0 1.0 2.0 1.0 0.0 3.0 0.0 0.0 -308425708.0 0.0 -46136631.0 -5781012.0 0.0 -349708.0 0.0 0.0 1333673.0 0.0 -4050805.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -18748.0 0.0 0.0
2282 12451508 Starlight 6239135 100 Greatest Pride Songs 2020-07-01 Muse GB United Kingdom 462882.0 Pop 1 7131300.0 123392.0 0.0 68.0 466.0 4283.0 -6763894.0 -123030.0 0.0 -1.0 400.0 4461.0 9484508.0 148401.0 12179814.0 -2.0 -2364.0 3965.0 -9157858.0 -144628.0 12115074.0 3.0 1847.0 3818.0 Alternative 0.0 0.0 435058.0 0.0 7642535.0 0.0 0.0 9404042.0 442622.0 0.0 10234865.0 0.0 0.0 85349.0 4446.0 0.0 131494.0 0.0 0.0 147580.0 4135.0 0.0 154036.0 0.0 0.0 0.0 0.0 0.0 0.0 3942820.0 3796329.0 4306314.0 4077171.0 4169383.0 3925369.0 4020322.0 0.0 1.0 2.0 0.0 0.0 41.0 0.0 0.0 0.0 2.0 0.0 1.0 0.0 164.0 144.0 145.0 142.0 113.0 119.0 0.0 0.0 452.0 2396.0 0.0 0.0 1305.0 1552.0 1496.0 1531.0 1434.0 1366.0 1391.0 1208.0 1251.0 1268.0 1299.0 0.0 3788701.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -9319832.0 0.0 6290037.0 3496380.0 5867007.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 903.0 0.0 0.0 0.0 0.0 1346.0 0.0 1944.0 0.0 2012.0
5178 15758732 Don't Bring Me Down 169825 Definitive Collection 1992-01-01 Electric Light Orchestra GB United Kingdom 462884.0 Rock 1 2706997.0 17577.0 0.0 3.0 406.0 2063.0 -2088818.0 -14917.0 0.0 21.0 385.0 2096.0 2885610.0 24149.0 13010157.0 -2.0 -1746.0 1984.0 571152.0 6038.0 12023829.0 2.0 2199.0 1846.0 Pop 0.0 0.0 2225506.0 0.0 35312.0 29089.0 2581973.0 160070.0 143567.0 0.0 3074658.0 214339.0 0.0 0.0 17206.0 0.0 653.0 457.0 20595.0 1800.0 1754.0 0.0 25036.0 2343.0 0.0 0.0 0.0 0.0 0.0 4316789.0 4587710.0 4549282.0 3873165.0 4425760.0 3899784.0 3698285.0 0.0 0.0 3.0 0.0 22.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 99.0 170.0 159.0 123.0 103.0 124.0 0.0 0.0 2078.0 0.0 0.0 0.0 666.0 722.0 713.0 712.0 671.0 667.0 651.0 666.0 685.0 583.0 578.0 0.0 0.0 0.0 1914827.0 0.0 1187855.0 0.0 0.0 0.0 197009.0 0.0 0.0 -8380023.0 0.0 2101057.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 930.0 -82.0 0.0 0.0 1173.0 0.0 1002.0 283.0 0.0 0.0
13528 33648905 Starlight 7780009 World's Most Stressed Out Gardener 2021-03-19 Chad VanGaalen CA Canada 462884.0 Rock 1 0.0 0.0 0.0 0.0 0.0 12.0 0.0 0.0 0.0 33.0 0.0 18.0 0.0 0.0 0.0 -20.0 0.0 8.0 0.0 0.0 0.0 1.0 1.0 8.0 Electronic 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11879.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 42.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 10.0 8.0 0.0 2.0 5.0 1.0 2.0 1.0 5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Spotify cluster 2 Country¶

In [123]:
df_spotify_c2 = two_s.COUNTRY_NAME.value_counts()
df_spotify_c2 = pd.DataFrame(data=df_spotify_c2)
df_spotify_c2.columns = ['count']
df_spotify_c2.plot.bar()
Out[123]:
<AxesSubplot:>

Spotify cluster 2 Genre¶

In [124]:
df_spotify_c2_genre = two_s.ARTIST_GENRE.value_counts()
df_spotify_c2_genre = pd.DataFrame(data=df_spotify_c2_genre)
df_spotify_c2.columns = ['count']
df_spotify_c2.plot.bar()
Out[124]:
<AxesSubplot:>
In [125]:
### Investigate cluster 3

df_3_s = df_no_zero_spotify.query('cluster == 3')
list_3_s = df_3_s.index.tolist()

three_s = df.query("TRACK_NAME in @list_3_s")
three_s.head()
Out[125]:
CM_TRACK TRACK_NAME CM_ALBUM ALBUM_NAME RELEASE_DATE ARTIST_NAME ARTIST_COUNTRY_CODE2 COUNTRY_NAME GENRE_ID ARTIST_GENRE DUPE Q1_YOUTUBE_VIEWS_GROWTH Q1_YOUTUBE_LIKES_GROWTH Q1_SPOTIFY_PLAYS_GROWTH Q1_SPOTIFY_POPULARITY_GROWTH Q1_TIKTOK_POSTS_GROWTH Q1_AIRPLAY_STREAMS_GROWTH Q2_YOUTUBE_VIEWS_GROWTH Q2_YOUTUBE_LIKES_GROWTH Q2_SPOTIFY_PLAYS_GROWTH Q2_SPOTIFY_POPULARITY_GROWTH Q2_TIKTOK_POSTS_GROWTH Q2_AIRPLAY_STREAMS_GROWTH Q3_YOUTUBE_VIEWS_GROWTH Q3_YOUTUBE_LIKES_GROWTH Q3_SPOTIFY_PLAYS_GROWTH Q3_SPOTIFY_POPULARITY_GROWTH Q3_TIKTOK_POSTS_GROWTH Q3_AIRPLAY_STREAMS_GROWTH Q4_YOUTUBE_VIEWS_GROWTH Q4_YOUTUBE_LIKES_GROWTH Q4_SPOTIFY_PLAYS_GROWTH Q4_SPOTIFY_POPULARITY_GROWTH Q4_TIKTOK_POSTS_GROWTH Q4_AIRPLAY_STREAMS_GROWTH GENRE M1_YOUTUBE_VIEWS_GROWTH M2_YOUTUBE_VIEWS_GROWTH M3_YOUTUBE_VIEWS_GROWTH M4_YOUTUBE_VIEWS_GROWTH M5_YOUTUBE_VIEWS_GROWTH M6_YOUTUBE_VIEWS_GROWTH M7_YOUTUBE_VIEWS_GROWTH M8_YOUTUBE_VIEWS_GROWTH M9_YOUTUBE_VIEWS_GROWTH M10_YOUTUBE_VIEWS_GROWTH M11_YOUTUBE_VIEWS_GROWTH M12_YOUTUBE_VIEWS_GROWTH M1_YOUTUBE_LIKES_GROWTH M2_YOUTUBE_LIKES_GROWTH M3_YOUTUBE_LIKES_GROWTH M4_YOUTUBE_LIKES_GROWTH M5_YOUTUBE_LIKES_GROWTH M6_YOUTUBE_LIKES_GROWTH M7_YOUTUBE_LIKES_GROWTH M8_YOUTUBE_LIKES_GROWTH M9_YOUTUBE_LIKES_GROWTH M10_YOUTUBE_LIKES_GROWTH M11_YOUTUBE_LIKES_GROWTH M12_YOUTUBE_LIKES_GROWTH M1_SPOTIFY_PLAYS_GROWTH M2_SPOTIFY_PLAYS_GROWTH M3_SPOTIFY_PLAYS_GROWTH M4_SPOTIFY_PLAYS_GROWTH M5_SPOTIFY_PLAYS_GROWTH M6_SPOTIFY_PLAYS_GROWTH M7_SPOTIFY_PLAYS_GROWTH M8_SPOTIFY_PLAYS_GROWTH M9_SPOTIFY_PLAYS_GROWTH M10_SPOTIFY_PLAYS_GROWTH M11_SPOTIFY_PLAYS_GROWTH M12_SPOTIFY_PLAYS_GROWTH M1_SPOTIFY_POPULARITY_GROWTH M2_SPOTIFY_POPULARITY_GROWTH M3_SPOTIFY_POPULARITY_GROWTH M4_SPOTIFY_POPULARITY_GROWTH M5_SPOTIFY_POPULARITY_GROWTH M6_SPOTIFY_POPULARITY_GROWTH M7_SPOTIFY_POPULARITY_GROWTH M8_SPOTIFY_POPULARITY_GROWTH M9_SPOTIFY_POPULARITY_GROWTH M10_SPOTIFY_POPULARITY_GROWTH M11_SPOTIFY_POPULARITY_GROWTH M12_SPOTIFY_POPULARITY_GROWTH M1_TIKTOK_POSTS_GROWTH M2_TIKTOK_POSTS_GROWTH M3_TIKTOK_POSTS_GROWTH M4_TIKTOK_POSTS_GROWTH M5_TIKTOK_POSTS_GROWTH M6_TIKTOK_POSTS_GROWTH M7_TIKTOK_POSTS_GROWTH M8_TIKTOK_POSTS_GROWTH M9_TIKTOK_POSTS_GROWTH M10_TIKTOK_POSTS_GROWTH M11_TIKTOK_POSTS_GROWTH M12_TIKTOK_POSTS_GROWTH M1_AIRPLAY_STREAMS_GROWTH M2_AIRPLAY_STREAMS_GROWTH M3_AIRPLAY_STREAMS_GROWTH M4_AIRPLAY_STREAMS_GROWTH M5_AIRPLAY_STREAMS_GROWTH M6_AIRPLAY_STREAMS_GROWTH M7_AIRPLAY_STREAMS_GROWTH M8_AIRPLAY_STREAMS_GROWTH M9_AIRPLAY_STREAMS_GROWTH M10_AIRPLAY_STREAMS_GROWTH M11_AIRPLAY_STREAMS_GROWTH M12_AIRPLAY_STREAMS_GROWTH WK1_YT_VIEWS_GROWTH WK2_YT_VIEWS_GROWTH WK3_YT_VIEWS_GROWTH WK4_YT_VIEWS_GROWTH WK5_YT_VIEWS_GROWTH WK6_YT_VIEWS_GROWTH WK7_YT_VIEWS_GROWTH WK8_YT_VIEWS_GROWTH WK9_YT_VIEWS_GROWTH WK10_YT_VIEWS_GROWTH WK11_YT_VIEWS_GROWTH WK12_YT_VIEWS_GROWTH WK13_YT_VIEWS_GROWTH WK14_YT_VIEWS_GROWTH WK15_YT_VIEWS_GROWTH WK1_SPOTIFY_PLAYS_GROWTH WK2_SPOTIFY_PLAYS_GROWTH WK3_SPOTIFY_PLAYS_GROWTH WK4_SPOTIFY_PLAYS_GROWTH WK5_SPOTIFY_PLAYS_GROWTH WK6_SPOTIFY_PLAYS_GROWTH WK7_SPOTIFY_PLAYS_GROWTH WK8_SPOTIFY_PLAYS_GROWTH WK9_SPOTIFY_PLAYS_GROWTH WK10_SPOTIFY_PLAYS_GROWTH WK11_SPOTIFY_PLAYS_GROWTH WK12_SPOTIFY_PLAYS_GROWTH WK13_SPOTIFY_PLAYS_GROWTH WK14_SPOTIFY_PLAYS_GROWTH WK15_SPOTIFY_PLAYS_GROWTH WK1_TIKTOK_POSTS_GROWTH WK2_TIKTOK_POSTS_GROWTH WK3_TIKTOK_POSTS_GROWTH WK4_TIKTOK_POSTS_GROWTH WK5_TIKTOK_POSTS_GROWTH WK6_TIKTOK_POSTS_GROWTH WK7_TIKTOK_POSTS_GROWTH WK8_TIKTOK_POSTS_GROWTH WK9_TIKTOK_POSTS_GROWTH WK10_TIKTOK_POSTS_GROWTH WK11_TIKTOK_POSTS_GROWTH WK12_TIKTOK_POSTS_GROWTH WK13_TIKTOK_POSTS_GROWTH WK14_TIKTOK_POSTS_GROWTH WK15_TIKTOK_POSTS_GROWTH
4466 13436558 Por Mujeres Como TĂș 18609450 Rolitas Bien Padres 2022-01-26 Pepe Aguilar MX Mexico 462957.0 Latin Pop 1 154277064.0 36166.0 0.0 72.0 186.0 73.0 8472815.0 30311.0 0.0 -63.0 422.0 77.0 6386299.0 25554.0 9594220.0 61.0 558.0 87.0 6756783.0 33528.0 10588867.0 4.0 303.0 79.0 Latin 0.0 3655001.0 3904724.0 3308060.0 3002850.0 2161905.0 2395815.0 2165736.0 1824748.0 1871438.0 1985950.0 2899395.0 0.0 10726.0 12404.0 10952.0 10852.0 8507.0 8961.0 8454.0 8139.0 8460.0 10152.0 14916.0 0.0 0.0 0.0 0.0 0.0 2998733.0 3084653.0 3110529.0 3399038.0 3503354.0 3199483.0 3886030.0 0.0 0.0 72.0 0.0 0.0 5.0 61.0 0.0 1.0 2.0 0.0 3.0 0.0 0.0 149.0 90.0 150.0 182.0 0.0 997.0 176.0 0.0 1378.0 123.0 0.0 25.0 40.0 22.0 23.0 32.0 30.0 31.0 26.0 26.0 28.0 25.0 0.0 117396701.0 126393135.0 0.0 0.0 147087224.0 0.0 0.0 -187033377.0 0.0 0.0 0.0 0.0 0.0 155602536.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -2762.0 0.0 0.0 0.0 0.0 0.0 203.0
5526 13300817 Dragostea Din Tei 1237695 DiscO-Zone 2002-01-01 O-Zone MD Moldova 462885.0 Dance 1 81004429.0 934846.0 0.0 59.0 315.0 1269.0 13417071.0 156665.0 0.0 -11.0 273.0 1308.0 13350982.0 146929.0 12009520.0 7.0 236.0 1141.0 11323738.0 118562.0 10558977.0 2.0 135.0 602.0 Dance 0.0 13778871.0 5888334.0 5036098.0 4478335.0 3902638.0 0.0 3500532.0 104266207.0 3409393.0 4055780.0 3858565.0 0.0 591299.0 87374.0 57318.0 53618.0 45729.0 0.0 61999.0 1207617.0 34721.0 44889.0 38952.0 0.0 0.0 0.0 0.0 0.0 4098314.0 4029713.0 4377800.0 3602007.0 3497895.0 3379441.0 3681641.0 0.0 12.0 3.0 0.0 0.0 0.0 9.0 0.0 55.0 2.0 0.0 1.0 0.0 103.0 144.0 129.0 93.0 51.0 71.0 111.0 54.0 0.0 0.0 2067.0 0.0 407.0 530.0 467.0 393.0 448.0 395.0 428.0 318.0 218.0 182.0 202.0 -3313122.0 56155653.0 59374575.0 0.0 31692149.0 0.0 -18077542.0 0.0 0.0 75342819.0 0.0 0.0 57100892.0 0.0 80795174.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1050.0 0.0 0.0 0.0 1169.0 0.0 0.0 0.0 0.0 1380.0 1174.0 -4558.0 1043.0
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15229 15812253 It's Raining Men 5088176 NOW That's What I Call Music! 3 2019-07-19 The Weather Girls US United States 462882.0 Pop 1 0.0 0.0 0.0 53.0 0.0 2300.0 0.0 0.0 0.0 7.0 0.0 1922.0 0.0 0.0 11245886.0 -1.0 0.0 287.0 0.0 0.0 10517458.0 2.0 0.0 1039.0 Electronic 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3935463.0 3553331.0 4135340.0 3557215.0 3586905.0 3320198.0 3610355.0 0.0 0.0 1.0 0.0 8.0 0.0 0.0 0.0 31.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 735.0 783.0 734.0 627.0 561.0 90.0 109.0 88.0 79.0 873.0 87.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
21133 15812112 It's Raining Men 3952673 Success 1982-01-01 The Weather Girls US United States 462889.0 R&B/Soul 1 0.0 0.0 0.0 15.0 0.0 309.0 0.0 0.0 0.0 41.0 0.0 317.0 0.0 0.0 2282069.0 1.0 0.0 279.0 0.0 0.0 2828489.0 5.0 0.0 230.0 Electronic 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 630073.0 667773.0 825450.0 788846.0 930638.0 879615.0 1018236.0 0.0 0.0 0.0 10.0 29.0 2.0 0.0 0.0 33.0 3.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 96.0 122.0 112.0 109.0 96.0 137.0 75.0 67.0 94.0 82.0 54.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Spotify Cluster 3 Country¶

In [126]:
df_spotify_c3 = three_s.COUNTRY_NAME.value_counts()
df_spotify_c3 = pd.DataFrame(data=df_spotify_c3)
df_spotify_c3.columns = ['count']
df_spotify_c3.plot.bar()
Out[126]:
<AxesSubplot:>

Spotify Cluster 3 Genre¶

In [127]:
df_spotify_c3_genre = three_s.ARTIST_GENRE.value_counts()
df_spotify_c3_genre = pd.DataFrame(data=df_spotify_c3_genre)
df_spotify_c3_genre.columns = ['count']
df_spotify_c3_genre.plot.bar()
Out[127]:
<AxesSubplot:>
In [ ]: